Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods

Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods Imran Hussain Shah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7808569/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract This study examines the evolving safe-haven properties and diversification benefits of digital assets across two distinct phases of global financial uncertainty: the COVID-19 crisis (January 2020–December 2021) and the post-COVID recovery period (January 2022–December 2023). Using GARCH (1,1) models and comparative volatility analysis, the research assesses the risk management performance of Bitcoin (BTC), Ethereum (ETH), and two leading stablecoins (USDC and USDT) under systemic stress. The findings reveal that while Bitcoin and Ethereum experienced high volatility persistence and limited hedging effectiveness during the crisis, stablecoins consistently provided low-variance characteristics, with USDC demonstrating remarkable resilience. Post-COVID results confirm a structural transition, as digital assets displayed improved volatility dynamics but continued to pose regulatory and systemic concerns. Unlike earlier studies that were restricted to the crisis period, this paper provides the first extended two-phase analysis, connecting empirical evidence to the global regulatory discourse, including debates on stablecoin oversight, systemic risk buffers, and financial stability frameworks. By integrating asset-level volatility outcomes with policy implications, the study contributes to understanding the dual role of digital assets as both high-risk diversifiers and potential regulatory instruments. The results provide valuable insights for investors, central banks, and policymakers in designing post-pandemic financial resilience strategies. Business and commerce/Finance Social science/Finance Physical sciences/Mathematics and computing Digital assets haven portfolio diversification volatility clustering GARCH model Bitcoin (BTC) Ethereum (ETH) stablecoins (USDC USDT) COVID-19 crisis post-COVID financial markets Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction 1.1 Introduction & Background The COVID-19 pandemic led to significant changes in global markets, prompting a rapid increase in the use of digital assets. This raised questions about whether these assets could act as safe places for investors during uncertain times. Earlier studies examined Bitcoin and Ethereum during the crisis years (2020–2021), but most didn’t investigate what happened after the pandemic ended and new rules began to be discussed. This paper fills that gap by examining how Bitcoin, Ethereum, and two main stablecoins (USDC and USDT) performed during both the crisis and recovery periods (2020–2023). It employs a specific model, known as the GARCH (1,1) model, to analyze its behavior. The results show that cryptocurrencies offered only a small benefit for diversifying a portfolio, as they remained highly volatile. However, stablecoins, especially USDC, acted as safer investments with less risk. By examining the time period beyond the crisis and connecting these findings to key discussions in global policy, such as those from the IMF, FSB, and the EU’s MiCA framework, this study demonstrates how digital assets can serve two roles: helping to diversify portfolios and also influencing financial regulations. The unique aspect of this study is that it provides a long-term, comparative perspective, linking the volatility of these assets to the development of economic stability policies in the aftermath of the pandemic. 1.2 Research Gap Existing research gives unclear answers about whether digital assets can serve as safe havens. Some studies suggest that Bitcoin may act as a hedge during significant market downturns, while other studies indicate that it behaves more like a high-risk investment, closely tied to stock prices. Ethereum has not been extensively studied in this area, despite its rapid growth and widespread adoption. Stablecoins, which are commonly used for trading and providing liquidity, have not been much studied as possible safe havens either. Additionally, most studies focus solely on the period during and immediately after the COVID-19 crisis, without examining whether digital assets continue to exhibit haven qualities when market swings return to normal. There's a big gap in research when it comes to comparing different types of digital assets (like cryptocurrencies and stablecoins) and looking at different market periods (like crisis and recovery). 1.3 Problem Statement Although digital assets are receiving increasing attention in both academic studies and real-world discussions, considerable uncertainty remains about whether they can serve as safe investments during significant financial crises. Cryptocurrencies are highly unpredictable and often perceived as a risky investment, yet they are still referred to as "digital gold." Stablecoins are designed to maintain their value stability. Still, it's unclear how well they can withstand sudden market changes and whether they contribute to the overall stability of the financial system. This issue is exacerbated by the lack of research comparing how these assets perform under different financial scenarios. If these gaps aren't filled, investors may invest in digital assets based on incorrect assumptions about their safety, and regulators may not fully understand the broader implications of using stablecoins. 1times of financial uncertainty study seeks to address the following central research question: Do digital assets—specifically Bitcoin, Ethereum, USDC, and USDT—serve as safe havens and provide portfolio diversification benefits during periods of financial stress, such as the COVID-19 crisis, and how does their role change in the post-COVID recovery period? From this central question, several sub-questions emerge: How do the volatility and persistence of digital assets differ across the COVID and post-COVID periods? Do stablecoins demonstrate stronger haven characteristics than cryptocurrencies? How do correlations between digital assets and traditional haven instruments (e.g., gold, Treasuries) evolve during crisis and recovery? What implications do these findings carry for investors, portfolio managers, and regulators? 1.5 Research Objectives The objectives of this study are fourfold: To analyze the volatility behavior of Bitcoin, Ethereum, USDC, and USDT during the COVID-19 crisis and the post-COVID recovery period, focusing on volatility clustering and persistence using GARCH-type models. To examine the correlation dynamics between digital assets and traditional financial instruments to assess their hedging and diversification potential. To compare the haven properties of cryptocurrencies versus stablecoins, highlighting their differences in risk and stability. To draw practical and policy-relevant implications for investors, regulators, and policymakers regarding the inclusion of digital assets in portfolios and financial stability frameworks. 2. Literature Review Safe-haven assets have always been important in financial economics, particularly when market uncertainty or stress is high. Investors typically turn to assets such as gold, U.S. Treasury securities, and other low-risk investments to safeguard their wealth during challenging financial times. However, with the rise of digital assets such as Bitcoin and Ethereum, as well as stablecoins like USDC and USDT, people are wondering if these can also serve as safe havens. The start of the COVID-19 pandemic in early 2020 created a significant global shock, providing researchers with an opportunity to test whether digital assets can serve as safe havens during both crisis and recovery periods. Some studies suggest that Bitcoin and Ethereum can help mitigate risk in extreme situations. Still, others highlight their high volatility and tendency to move in tandem with other high-risk assets. On the other hand, stablecoins are designed to maintain a stable value by maintaining a fixed relationship with fiat currencies, making them more reliable. However, there isn’t much real-world evidence yet about how they perform during tough times. 2.1 Safe Haven Theory and Portfolio Diversification Baur and Lucey [1] discuss two types of assets: hedge assets, which don't move significantly with stocks, and safe-haven assets, which act as a shelter during turbulent times. Gold is often seen as the classic haven across different markets [2]. U.S. Treasuries also serve as a hedge because they typically move in the opposite direction of riskier investments during adverse market conditions [6]. Markowitz [7] explained that diversifying a portfolio with assets that have low or negative correlations helps reduce overall risk. Erb and Harvey [8] emphasize the importance of commodities in a strategy. At the same time, Ang and Bekaert [9] suggest using models that adapt to different market situations to better understand how correlations shift during crises. Recently, cryptocurrencies have raised questions about the effectiveness of diversification. Dyhrberg [10] studied Bitcoin using GARCH models and found it has some similarities with gold and the U.S. dollar, suggesting it might act like a haven. However, because Bitcoin is highly volatile, it's unclear whether it can reliably serve as a hedge. 2.2 Cryptocurrencies and Volatility Dynamics Bitcoin's price fluctuations have been closely examined, often indicating speculative bubbles and periods of high volatility. Cheah and Fry [3] viewed Bitcoin as an asset that tends to form bubbles, whereas Katsiampa [11] found that volatility in Bitcoin is persistent, utilizing models such as GARCH. Corbet et al. [12] demonstrated that cryptocurrencies are not distinct from other financial markets, closely tracking stocks, commodities, and currencies. Ethereum, the second-biggest cryptocurrency, has not been studied as much, but it has its own unique features. Liu and Tsyvinski [13] examined the risks and returns of digital assets, finding that Ethereum is more closely tied to stock markets than Bitcoin. Klein et al. [14] said that neither Bitcoin nor Ethereum is as stable as gold, which weakens their claim of being a safe investment during tough times. Urquhart [15] also noted that inefficiencies in cryptocurrency markets make them more speculative. The COVID-19 crisis provided a real-life test of these market behaviors. Conlon et al. [4] suggested that Bitcoin did not act as a safe investment in March 2020 and instead increased the risk in portfolios. Goodell and Goutte [16] employed wavelet coherence analysis to demonstrate that Bitcoin's movement was correlated with the number of COVID-19 cases, suggesting that volatility was driven by public sentiment rather than being a safe asset. These results highlight the limitations of relying on cryptocurrencies as a reliable source of protection during major crises. 2.3 Stablecoins and Financial Stability Stablecoins like USDC and USDT differ from regular cryptocurrencies because their values are tied to a real currency, such as the US dollar. This link helps keep their prices steady and less likely to fluctuate significantly [17]. Studies show that stablecoins are essential in the crypto world because they help keep markets running smoothly by providing liquidity [18]. However, questions remain about the clarity of their financial backing and whether they are truly backed by real money [19]. Arner and others [20] warn that if stablecoins proliferate without proper rules, it could create significant problems for the financial system. In times of trouble, stablecoins are intended to be a safer choice, allowing people to move money out of risky investments without relying on traditional banks. Lyons and Viswanath-Natraj [21] found that when things get tough, people tend to use stablecoins more, indicating a preference for stability. However, USDT has been questioned about whether it has sufficient real-world value to back its price, which has caused some distrust in the market [22]. On the other hand, USDC is perceived as more transparent and trustworthy, making it a better choice for individuals seeking a secure alternative [23]. 2.4 Digital Assets During COVID-19 The COVID-19 pandemic presented researchers with a real-world opportunity to test digital assets as tools for mitigating financial risks. Studies show that cryptocurrencies behave differently. One study found that Bitcoin posed more risk than a safe investment [24], while another showed that digital assets and stock markets became increasingly connected during the pandemic. However, another study highlighted that in certain areas, digital assets offer benefits by spreading out risks [25], indicating that their performance varies depending on the region. Stablecoins, which are less studied, showed more strength. Research has found that stablecoins maintained their value during the significant market drop in March 2020, which supports their use as a substitute for cash. But events like the temporary loss of value in USDT show that they are not without problems. Since stablecoins are not well-studied in academic research, there is a lack of understanding about their role in financial systems during periods of economic stress. 2.5 Post-COVID Shifts in Digital Assets Following the pandemic, the economy underwent significant changes, driven by rising prices, tighter monetary policies, and market adjustments. Research indicates that, after 2021, the relationship between cryptocurrencies and traditional investments weakened [26], suggesting that cryptocurrencies may offer more effective ways to diversify risk. Ethereum, in particular, has exhibited less volatility over time, indicating that the market is becoming more stable [27]. However, cryptocurrencies still exhibit many speculative traits, so they do not yet fully act as safe investments. Stablecoins, however, have become more trusted as reliable tools. USDC, for example, is now widely used for making payments and settling transactions [28]. New rules from organizations such as the European Union and the U.S. have underscored the importance of stablecoins for the overall financial system [29]. These changes suggest that while cryptocurrencies may continue to be used for diversification, stablecoins could eventually be viewed as reliable safe havens if properly managed. 2.6 Research Gap The research suggests that three primary areas require additional attention. First, most studies focus on Bitcoin, but they pay little attention to Ethereum, and even less to how cryptocurrencies compare with stablecoins within a single, clear framework. Second, much of the work focuses solely on the time during the COVID-19 crisis, and it overlooks what happens after the crisis, which may alter how people perceive these assets as safe havens. Third, the methods employed are inconsistent—some use simple correlations, while others employ complex econometric models, resulting in mixed findings. This study fills these gaps by employing basic statistics, correlation analysis, and GARCH models to examine Bitcoin, Ethereum, USDC, and USDT during both the pandemic and its aftermath, providing a more comprehensive view that helps both scholars and practitioners gain a better understanding. Unlike most earlier studies, which examined only the COVID-19 crisis period, our research provides a two-phase comparative analysis—covering both the crisis (2020–2021) and the post-COVID recovery period (2022–2023) —to identify how the safe-haven and diversification roles of digital assets evolved over time. Previous works, such as Conlon et al. (2020) and Goodell and Goutte (2021) , focused primarily on the pandemic shock and found that Bitcoin failed to act as a haven, often amplifying portfolio risk. Similarly, Bouri et al. (2017) and Dyhrberg (2016) examined Bitcoin in isolation, without differentiating between asset classes or subsequent market phases. In contrast, our paper introduces a comparative framework that evaluates not only cryptocurrencies (such as Bitcoin and Ethereum) but also stablecoins (including USDC and USDT)—an area that has been largely overlooked in existing literature. Furthermore, previous research rarely linked empirical volatility evidence to policy and regulatory debates . In contrast, our study integrates findings with global frameworks, including the IMF’s digital finance guidelines, the FSB’s systemic risk directives, and the EU’s MiCA regulation . Hence, while earlier studies treated digital assets as a single category and focused on short-term crisis reactions, this study offers a long-horizon, cross-asset, and policy-connected approach , advancing both academic and practical understanding of digital assets in the context of post-pandemic financial resilience. 3. Methods and Data 3.1 Research Design This study employs a quantitative research method and applies econometric models to examine how digital assets serve as a haven and provide diversification benefits during two distinct market periods: the COVID-19 crisis from January 2020 to December 2021, and the recovery period from January 2022 to December 2023. Choosing a quantitative approach involves considering previous research on how assets are priced and how volatility is modeled, which focuses on identifying statistical connections between asset returns and broader economic and financial factors. The study employs tools such as descriptive statistics, correlation tables, and GARCH-type models to investigate the relationship between digital assets and traditional safe-haven assets, as well as overall market uncertainty. By examining both the crisis and recovery periods, the study provides a broader perspective, helping to overcome the limitation of earlier studies that focused solely on short-term crises. 3.2 Data Description The dataset includes monthly data from January 2020 to December 2023. This period is divided into two parts: the time during the COVID-19 crisis (January 2020 to December 2021) and the time after the crisis (January 2022 to December 2023). Four digital assets are chosen: Bitcoin (BTC) and Ethereum (ETH) as examples of cryptocurrencies, and USDC and USDT as the most commonly used stablecoins. Including these options allows for a comparison between more speculative assets and those that aim to maintain stable value. In addition to these digital assets, the study also examines traditional safe-haven investments for comparison, including gold, U.S. Treasury yields, and the S&P 500 index. The analysis also includes macroeconomic and financial indicators, such as the U.S. inflation rate, interest rates, the U.S. dollar index, and the VIX (Volatility Index). These help in understanding overall risk and economic conditions. All price and index information is sourced from reputable and publicly accessible sources, including Yahoo Finance, Federal Reserve Economic Data (FRED), CoinMarketCap, and Investing.com. Monthly log returns are calculated as: Where PtP_tPt represents the closing price or index value at time t. This transformation ensures stationarity and reduces heteroskedasticity in the return series [4]. 3.3 Variables and Measurements The variables are grouped into dependent and independent categories. Dependent Variables: Log returns of digital assets (BTC, ETH, USDC, USDT). These capture the main dynamics under investigation, enabling the modeling of volatility and testing for safe havens. Independent Variables: Log returns of benchmark assets and macro indicators. Gold returns – proxy for traditional haven. S&P 500 returns – proxy for equity market performance. Treasury yields – benchmark for fixed-income safe havens. Inflation (CPI) – proxy for price stability. Policy rates – represent monetary tightening/loosening. U.S. Dollar Index – measures currency strength. VIX – captures global market uncertainty. By including these variables, the models assess whether digital assets behave like safe havens (negatively correlated with risky assets during crises) or speculative assets (positively correlated with equities). To analyze volatility and persistence, the study employs GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, which are widely used in financial econometrics for modeling time-varying variance [5]. The GARCH (1,1) specification is given as: Rt = In (P t /P (t-1) ) where PtP_tPt is the daily closing price at time ttt. Independent Variables: Market volatility, macroeconomic shocks, and asset-specific risk factors serve as independent variables. For digital assets, volatility proxies are derived using GARCH (1,1), EGARCH, TGARCH, and GJR-GARCH models. For traditional safe-haven assets (gold, US Treasuries), historical volatility is used for comparison. Control Variables: Inflation rate, policy rate changes, and global risk aversion indices (such as the VIX) are included to account for macro-financial influences. All return series are tested for stationarity using Augmented Dickey-Fuller (ADF) and Phillips–Perron (PP) unit root tests. Step 1: Descriptive Statistics & Correlations Descriptive measures (mean, variance, skewness, kurtosis, Jarque-Bera) provide insight into the statistical properties of returns. Correlation matrices compare co-movements of digital assets with traditional assets during the COVID and post-COVID periods. Step 2: GARCH Estimation GARCH (1,1) is estimated separately for BTC, ETH, USDC, and USDT. RBtci = μ + β1rSP500t + β2rGoldt + B3rInflationt + B4rPRt + B5rTRt + B6rUSDIt + B7rVIXt Diagnostics (AIC, SC, log likelihood, Durbin-Watson) assess model fit. Stability is confirmed using ARCH-LM tests to ensure no residual autocorrelation in variance. We have tested four models. The Dependent Variables only changed BTC, ETH, USDC, and USDT. Step 3: Comparative Analysis Compare coefficients across COVID vs post-COVID. Assess volatility clustering, persistence, and differences between cryptocurrencies and stablecoins. 3.5 Justification of Methods The GARCH framework is particularly suitable given the stylized facts of financial time series, including volatility clustering, fat tails, and heteroskedasticity. While alternatives such as EGARCH or TGARCH could account for asymmetry, the GARCH (1,1) remains the most parsimonious and widely adopted in studies of cryptocurrencies. Correlation analysis complements GARCH by offering a simple but powerful measure of diversification potential. Together, these methods provide both descriptive and dynamic insights. The decision to split the analysis into COVID and post-COVID periods enhances robustness, allowing direct comparison of asset behaviors under crisis and recovery conditions. This temporal segmentation aligns with prior research frameworks assessing haven dynamics across different phases of systemic stress. 3.6 Limitations The method used is thorough, but it has some limits. One issue is that using monthly data may miss sudden, large price swings that occur within a day or during the day. However, this choice was made because the data are available and the study spans an extended time period. Another limitation is that stablecoins are a relatively new concept, so there isn't much long-term history to work with, which makes it harder to build a robust model. Additionally, GARCH models assume that events follow a regular pattern, which may not accurately account for extremely rare or unusual situations. Future work could employ more advanced methods, such as multivariate GARCH, copula models, or models that adapt to different market conditions. Lastly, although the study includes essential economic and financial factors, it can't fully account for aspects that aren't measured, such as people's feelings about the market or the perceived trustworthiness of policies. 4. Empirical Results 4.1 Descriptive Statistics Table 1 presents descriptive statistics for Bitcoin, Ethereum, Gold, the S&P 500, Oil, and US Treasury Bonds. Bitcoin and Ethereum exhibit the highest mean returns but also the highest standard deviations, reflecting their volatile nature. Gold exhibits moderate average returns with low variance, consistent with its reputation as a haven. Skewness and kurtosis values indicate that Bitcoin and Ethereum have heavy-tailed distributions, confirming non-normality in their return series. Jarque–Bera tests reject the null hypothesis of normal distribution for all digital assets, justifying the use of GARCH-family models. Table 1 Appendix A Table 1 presents the basic statistical information on digital assets and traditional financial tools during the COVID-19 crisis period (2020–2021) and in the aftermath of the crisis (2022–2023). During the crisis, Bitcoin (BTC) and Ethereum (ETH) had high average returns but also significant fluctuations in their values, indicating that they are highly volatile. Both of these assets exhibited a positive skew and high kurtosis, indicating the presence of extreme returns and heavy-tailed distributions. Gold (GLDLR), on the other hand, exhibited moderate positive returns with lower volatility, suggesting that it acts as a traditional safe asset. The equity indices (SPLR, MSCIELR, MSCIALR) exhibited high kurtosis and positive skew, which is typical during periods of financial stress, while the VIX index displayed very high variance, indicating high uncertainty among investors. In the post-COVID period, volatility across most assets decreased, as indicated by lower standard deviations and less kurtosis. Bitcoin and Ethereum still had positive returns, but with smaller swings, which suggests the market was becoming more stable. Traditional safe assets, such as gold and bonds, maintained their low-risk profiles, while equity indices recovered with less skewness and kurtosis. Overall, the results indicate that while digital assets generated good returns during the crisis, their risk levels were still significantly higher than those of traditional safe havens, which limited their ability to serve as reliable safe assets compared to gold. 4.2 Correlation Analysis Correlation matrices reveal that during the Crisis Period , Bitcoin’s correlation with equities (S&P 500) turned weakly positive but remained significantly lower than that of traditional assets. Its correlation with gold was near zero, suggesting potential benefits from diversification. In contrast, in the post-COVID period , Bitcoin’s correlation with equities strengthened, reducing its hedging potential, while gold retained its negative correlation with equities. Table 2 Appendix A Table 2 illustrates how Bitcoin's connections with other investments evolved during two distinct periods: the COVID crisis and the subsequent recovery. During the crisis, which spanned from 2020 to 2021, Bitcoin exhibited weak to moderate positive correlations with stocks such as the S&P 500 (0.39) and MSCI Asia (0.48), indicating a mild connection to traditional markets. However, its link with gold was negative (-0.41), suggesting that it might have been a good choice to diversify risk. Bitcoin also had a moderate positive correlation with safe investments, such as Treasury bonds (0.41), indicating that it played both a speculative and a hedging role during uncertain times. In the period following the crisis, from 2022 to 2023, Bitcoin’s correlation with stocks increased significantly, as seen with the S&P 500 (0.52) and MSCI Europe (0.45), indicating that it became more closely tied to riskier investments and less of a haven. Its link with gold became much weaker, approaching neutrality (0.11), meaning it no longer acted like gold in protecting against losses. Interestingly, Bitcoin’s link with Ethereum increased significantly, from 0.20 to 0.60, indicating that in more stable times, the crypto market is becoming more interconnected within itself. Overall, the results suggest that Bitcoin exhibited some safe-haven qualities during the crisis. Still, during the post-COVID period, it became more closely linked with riskier assets, which weakens its ability to help diversify a portfolio. 4.3 GARCH Model Table 3 to 6 Table 3 GARCH Model 1 Dependent Variables: Bitcoin Log return COVID Period Jan 2020 to Dec 2021 Variable Coefficient Std. Error z-Statistic Prob. C -0.208053 0.069996 -2.97236 0.003 VIX_LOGRETURN -0.644188 0.466418 -1.38114 0.1672 INFLATION_LOGRETURN -31.08981 11.82973 -2.628107 0.0086 GOLD_LOGRETURN -0.957711 1.292735 -0.740841 0.4588 S_P_500_LOGRETURN -2.4143 2.442632 -0.988401 0.323 POLICY_RATES_LOGRETURN 0.15158 0.081426 1.861581 0.0627 TREASURIES_LOGRETURN 0.636246 0.317329 2.005008 0.045 Variance Equitation Variable Coefficient Std. Error z-Statistic Prob. C 0.010329 0.013419 0.769752 0.4414 RESID(-1)^2 (ARCH) -0.187875 0.146634 -1.281249 0.2001 GARCH(-1) 0.587602 1.094962 0.536641 0.5915 Model diagnostics : R² = 0.506; Adj. R² = 0.332; DW = 2.012; Log Likelihood = 18.92; AIC = –0.749; SC = –0.259; Obs = 24 Post COVID Period Jan 2022 to Dec 2023 Variable Coefficient Std. Error z-Statistic Prob. C –0.118704 0.045934 –2.584198 0.0098 VIX_LOGRETURN 0.602586 0.381015 1.581528 0.1138 INFLATION_LOGRETURN –33.88604 24.02921 –1.410222 0.1585 GOLD_LOGRETURN –0.140090 0.849057 –0.164994 0.8689 S_P_500_LOGRETURN 0.500011 0.706086 0.708136 0.4454 POLICY_RATES_LOGRETURN 0.2427 0.426563 0.568967 0.5694 TREASURIES_LOGRETURN –0.489463 0.376334 –1.300607 0.1934 Variance Equitation Variable Coefficient Std. Error z-Statistic Prob. C 0.003163 0.005946 0.531916 0.5948 RESID(-1)^2 (ARCH) –0.308945 0.200466 –1.541178 0.123 GARCH(-1) 1.16719 0.25614 4.556844 0 Model Diagnostics: R² = 0.256; Adj. R² = –0.072; DW = 2.316 Log Likelihood = 16.55; AIC = –0.546; SC = –0.126; Obs = 24 Table 3 presents the results from the GARCH model, highlighting significant changes in what affects Bitcoin’s returns before and after the COVID-19 crisis. During the crisis years (2020–2021), returns on Treasury bonds had a strong positive effect on Bitcoin, indicating that Bitcoin's value moved somewhat in line with safer government assets. However, changes in interest rates had only a weak effect. Other factors, such as stock prices (S&P 500), gold, and inflation, had no significant impact, indicating that Bitcoin was somewhat separate from traditional financial markets at that time. Examining the change in volatility, neither the ARCH nor GARCH terms were significant; however, the positive sign on GARCH (1) suggested some ongoing fluctuations. In the years following the crisis (2022–2023), none of the factors had a significant impact on Bitcoin’s returns, suggesting that Bitcoin’s performance became more distinct and less tied to broader financial trends. However, in the volatility part of the model, the GARCH (1) term was highly significant and positive, indicating that Bitcoin’s volatility exhibits strong persistence and long-term memory. Overall, these findings suggest that while Bitcoin was briefly linked to Treasury bonds during the crisis, after the crisis, it became more of a speculative asset driven by volatility, with returns less connected to traditional safe assets and overall economic conditions. 4.3.1 GARCH Model 2 Table 4 Table 4 GARCH Model 2 Dependent Variables: Ethereum Log return COVID Period Jan 2020 to Dec 2021 Variable Coefficient Std. Error z-Statistic Prob. C –0.232571 0.124868 –1.862544 0.0625 GOLD_LOGRETURN 2.338785 0.994741 2.351151 0.0187 INFLATION_LOGRETURN –28.98526 16.37508 –1.770084 0.0767 POLICY_RATES_LOGRETURN 0.016038 0.218599 0.073324 0.9417 S_P_500_LOGRETURN 1.386511 2.9645 0.467572 0.6401 TREASURIES_LOGRETURN 0.683548 0.224113 3.0482 0.0026 VIX_LOGRETURN 0.053088 0.449093 0.118122 0.9059 USD_INDEX_LOGRETURN –1.380252 4.053637 –0.340497 0.7335 Variance Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.014602 0.03433 0.42535 0.6706 RESID(-1)^2 (ARCH) –0.242594 0.368634 –0.658009 0.5105 GARCH(-1) 0.650783 0.356395 1.826088 0.0764 Model Diagnostics: R² = 0.510; Adj. R² = 0.297; DW = 2.081 Log Likelihood = 15.10; AIC = –0.342; SC = 0.035; Obs = 24 Post COVID Period Jan 2022 to Dec 2023 Variable Coefficient Std. Error z-Statistic Prob. C –0.027620 0.04706 –0.588903 0.5573 GOLD_LOGRETURN 0.67562 0.627855 1.076078 0.2819 INFLATION_LOGRETURN –19.43455 11.6146 –1.673286 0.0943* POLICY_RATES_LOGRETURN 0.105027 0.221464 0.474238 0.6353 S_P_500_LOGRETURN 3.750913 1.710115 2.193639 0.0283** TREASURIES_LOGRETURN 0.364612 0.712124 0.512654 0.6082 VIX_LOGRETURN 0.396062 0.419032 0.945184 0.3446 USD_INDEX_LOGRETURN 0.586296 0.056451 10.38587 0.0003*** Variance Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.004792 0.014901 0.321614 0.7477 RESID(-1)^2 (ARCH) –0.159000 0.707561 –0.224716 0.8222 GARCH(-1) 0.617915 1.474978 0.418931 0.6753 Model Diagnostics: R² = 0.614; Adj. R² = 0.445; DW = 1.536 Log Likelihood = 26.96; AIC = –1.329; SC = –0.790; Obs = 24 Table 4 shows that Ethereum has stronger and more flexible connections with traditional assets than Bitcoin. During the COVID crisis (2020–2021), Ethereum's returns were closely and positively linked to gold and US Treasury returns, meaning it behaved somewhat like traditional safe-haven assets. This is different from Bitcoin, which didn’t show strong safe-haven qualities during the same time. Also, the GARCH (1) term was positive and slightly significant, showing that Ethereum’s volatility tends to continue over time. In the time after the pandemic (2022–2023), Ethereum's behavior changed a lot: its returns became closely and positively connected to the S&P500, showing it's becoming more linked to stock markets. At the same time, the US dollar index became a very important factor, showing Ethereum is sensitive to overall financial conditions. Inflation had a weak but noticeable effect, showing Ethereum is influenced by broader economic factors. In the part about volatility, neither the ARCH nor GARCH terms were significant, though the GARCH (1) had a positive coefficient, suggesting that volatility patterns still cluster. Overall, these results show that Ethereum acted as a transitional asset—showing safe-haven traits during the crisis because of its connections with gold and Treasuries, but then becoming more like a risk-on asset after the crisis, as it became more connected to stocks and global economic factors. 4.3.2 GARCH Model 3 Table 5 Table 5 GARCH Model 3 Dependent Variables: USDC Log return COVID Period Jan 2020 to Dec 2021 Variable Coefficient Std. Error z-Statistic Prob. C –0.004964 0.003586 –1.384403 0.1662 GOLD_LOGRETURN –0.035947 0.039404 –0.912271 0.3616 INFLATION_LOGRETURN –0.960136 0.36027 –2.665043 0.0077 POLICY_RATES_LOGRETURN 0.002457 0.012999 0.188974 0.8501 S_P_500_LOGRETURN 0.0582 0.113675 0.51199 0.6087 TREASURIES_LOGRETURN 0.033749 0.011259 2.919814 0.0035 USD_INDEX_LOGRETURN –0.060865 0.093686 –0.649670 0.5159 VIX_LOGRETURN 0.018849 0.014765 1.276566 0.2018 Variance Equation Variable Coefficient Std. Error z-Statistic Prob. C 3.86E-06 5.69E-06 0.677783 0.498 RESID(-1)^2 (ARCH) 1.687282 0.842618 2.002427 0.0452 GARCH(-1) –0.014900 0.025205 –0.591130 0.5544 Model Diagnostics: R² = –0.042; Adj. R² = –0.498; DW = 3.178 Log Likelihood = 83.30; AIC = –6.025; SC = –5.485; Obs = 24 Post COVID Period Jan 2022 to Dec 2023 Variable Coefficient Std. Error z-Statistic Prob. C –2.47E-05 3.31E-05 –0.747 0.455 GOLD_LOGRETURN –0.000119 0.001355 –0.087 0.93 INFLATION_LOGRETURN –0.022636 0.020003 –1.132 0.258 POLICY_RATES_LOGRETURN –1.10E-06 0.000234 –0.004 0.996 S_P_500_LOGRETURN –0.000623 0.000962 –0.647 0.518 TREASURIES_LOGRETURN 0.000176 0.000166 1.059 0.29 USD_INDEX_LOGRETURN –0.000679 0.003632 –0.187 0.851 VIX_LOGRETURN –0.001248 0.000564 –2.220 0.026** Variance Equation Variable Coefficient Std. Error z-Statistic Prob. C 2.12E-08 2.97E-08 0.713 0.477 RESID(-1)^2 (ARCH) 0.991514 1.199109 0.826 0.402 GARCH(-1) –0.316327 0.518954 –0.609 0.456 Model Diagnostics: R² = 0.227; Adj. R² = –0.116; DW = 2.59 Log Likelihood = 15.09; AIC = –1.007; SC = –0.526; Obs = 24 Table 5 The GARCH results for USDC, a stablecoin, confirm its role as a low-volatility asset with minimal dependence on traditional market drivers. During the COVID-19 crisis (2020–2021), most explanatory variables were insignificant, except inflation (negative and significant) and Treasuries (positive and essential), suggesting that the USDC marginally reflected macroeconomic conditions and government bond dynamics. The variance equation reveals a significant ARCH term, indicating short-run volatility clustering, although the model's overall explanatory power was weak (R² = –0.042). In the post-COVID period (2022–2023), USDC returns remained unresponsive primarily to gold, equities, Treasuries, and policy rates, consistent with its peg to the US dollar. The only significant driver was the VIX, which showed a negative relationship, implying that USDC retained demand during periods of financial uncertainty. In the variance equation, neither the ARCH nor the GARCH terms were significant, confirming that volatility persistence was absent. Collectively, these results reinforce the stablecoin’s design: USDC behaves as a quasi-risk-free asset with negligible exposure to traditional market movements, making it an effective tool for liquidity preservation and short-term hedging during both crisis and recovery phases. 4.3.3 GARCH Model 4 Table 6 Table 6 GARCH Model Dependent Variables: USDT Log return COVID Period Jan 2020 to Dec 2021 Variable Coefficient Std. Error z-Statistic Prob. C –0.000268 0.000299 –0.897 0.369 GOLD_LOGRETURN –0.030968 0.000617 –50.22396 0.025 INFLATION_LOGRETURN –0.187085 0.002318 –80.71322 0.015 POLICY_RATES_LOGRETURN –0.001342 0.001137 –1.180633 0.237 S_P_500_LOGRETURN 0.065128 0.015453 4.177236 0.089 TREASURIES_LOGRETURN –0.002637 0.000482 –5.457318 0.013 USD_INDEX_LOGRETURN –0.065332 0.000472 –138.4135 0.028 VIX_LOGRETURN 0.004183 0.001436 2.914266 0.0036 Variance Equation Variable Coefficient Std. Error z-Statistic Prob. C –3.52E-09 4.67E-07 –0.007534 0.994 RESID(-1)^2 (ARCH) 42.28426 12.78787 3.306592 0.0009 GARCH(-1) –0.000452 0.008921 –0.050657 0.9596 Model Diagnostics: R² = –0.066; Adj. R² = –0.533; DW = 2.974 Log Likelihood = 18.32; AIC = –0.652; SC = –0.247; Obs = 24 Post COVID Period Jan 2022 to Dec 2023 Variable Coefficient Std. Error z-Statistic Prob. C –5.66E–05 0.000203 –0.278 0.781 GOLD_LOGRETURN –0.003178 0.00496 –0.641 0.52 INFLATION_LOGRETURN –0.073135 0.041324 –1.769 0.076 POLICY_RATES_LOGRETURN –0.000278 0.000289 –0.961 0.336 S_P_500_LOGRETURN –3.83E–06 0.005929 –0.001 0.999 TREASURIES_LOGRETURN 0.003005 0.00122 2.454 0.025** USD_INDEX_LOGRETURN 0.017717 0.013177 1.344 0.192 VIX_LOGRETURN –0.001326 0.00183 –0.723 0.469 Variance Equation Variable Coefficient Std. Error z-Statistic Prob. C 6.16E–08 8.48E–08 0.726 0.467 RESID(–1)^2 (ARCH) –0.154777 0.067353 –2.299 0.026** GARCH(–1) 0.621473 0.795128 0.781 0.434 Model Diagnostics: R² = 0.245; Adj. R² = –0.083; DW = 2.13 Log Likelihood = 163.96; AIC = –12.747; SC = –12.604; Obs = 24 Table 6 The GARCH results for USDT, another stablecoin, confirm its stability and resilience but also reveal distinct dynamics across the crisis and post-COVID phases. During the COVID period (2020–2021), several variables were statistically significant: USDT returns were negatively correlated with gold, Treasuries, and the USD index, while showing a positive association with equities (S&P 500) and the VIX. This suggests that, although designed to be stable, USDT exhibited mild sensitivity to broader market movements, particularly during periods of financial stress when demand for stable liquidity instruments increased. The variance equation highlights a highly significant ARCH effect, indicating short-run volatility clustering in USDT despite its peg mechanism. In the post-COVID period (2022–2023), most explanatory variables became insignificant, consistent with greater stabilization of the peg. The only significant driver was Treasuries, with a weak positive coefficient, reflecting minor alignment with government bond stability. The variance equation reveals a negative and significant ARCH effect, indicating that shocks to USDT volatility dissipate quickly, thereby strengthening its credibility as a stable store of value. Overall, these results confirm that while USDT displayed some sensitivity to market stress during the COVID crisis, it performed more consistently in the post-COVID period, supporting its role as a low-risk liquidity instrument within diversified portfolios. 4.4 Figure 1. Volatility of Bitcoin (BTC) during COVID-19 and Post-COVID Periods (2020–2023). 4.4.1 Figure 2. Volatility of Ethereum (ETH) across Crisis and Recovery Phases (2020–2023). 4.4.2 Figure 3. Volatility of USDC (Stablecoin) 2020–2023 4.4.3 Figure 4. Volatility of USDT (Stablecoin) 2020–2023. 4.5 Results Implications The study reveals that digital assets can play a role in diversifying investment portfolios and may serve as safe havens during periods of financial stress. However, the results suggest that Bitcoin and Ethereum experienced high returns during the crisis. Still, they also came with significant volatility and risks, which make them more akin to speculative investments rather than reliable safety nets. Initially, Bitcoin wasn't strongly linked to traditional safe-haven assets, such as gold or government bonds; however, as it became more closely tied to the broader financial system, its appeal as a haven gradually decreased over time. Ethereum had mixed results: it showed some safe-haven qualities during the crisis, but after the crisis, it performed more like stocks and other riskier assets, suggesting it is moving toward being a speculative asset. This means that while digital assets can offer some protection during times of extreme stress, they are not as reliable as long-term safe havens. Stablecoins like USDC and USDT, on the other hand, acted more like low-risk, stable cash equivalents. Their values remained relatively steady, except for some short-term spikes in 2021, particularly with USDT, which was linked to concerns about the reserves backing it. Because they had little connection to risky investments, stablecoins were effective at preserving capital and reducing overall portfolio risk during difficult times. The study highlights a key difference within digital assets: some are high-risk, volatile cryptocurrencies, while others, such as stablecoins, offer stability and predictability similar to those of money market funds. From a practical standpoint, investors should not treat all digital assets uniformly. Bitcoin and Ethereum might help diversify a portfolio during a crisis. Still, stablecoins like USDC and USDT offer more dependable protection against short-term financial shocks and could be considered in strategies aimed at economic stability. 4.6 Discussion The findings of this study offer important insights into the evolution of digital assets in financial markets, particularly their role as safe-haven assets and their capacity to diversify risk during times of crisis, including the aftermath of the COVID-19 pandemic. This finding aligns with earlier research by Baur and Lucey [30] and Dyhrberg [31], which also found that Bitcoin did not strongly act as a haven during the pandemic. This is evident in the weak or negative correlation between Bitcoin and stocks, and the moderate connection with Treasuries. However, this safe-haven role was short-lived; in the aftermath of the pandemic, Bitcoin became more closely tied to traditional markets, which reduced its ability to protect against significant financial risks. This change aligns with the views of Corbet et al. [32] and Goodell and Goutte [33], who argue that digital assets are evolving into more traditional financial products, acting more like investments that people bet on rather than tools for safety, as markets mature. Ethereum followed a similar but more changeable path. Initially, it had strong links to gold and Treasuries, suggesting some safe-haven qualities during crises. However, during recovery, Ethereum became more closely tied to stocks and major financial indicators, such as the US Dollar Index, indicating it had evolved into a risk-taking asset. These results suggest that while cryptocurrencies may act as temporary hedges during periods of global uncertainty, their long-term presence in financial markets reduces their ability to serve as a safe option. Stablecoins such as USDC and USDT, on the other hand, showed significantly different behavior. Both remained relatively unchanged throughout the study, indicating that they are trusted as tools to maintain liquidity. Short, quick spikes in 2021, especially with USDT, initially raised concerns about whether the reserves were sufficient and whether there was adequate regulation. Still, these issues soon subsided, and stability was restored. Unlike Bitcoin and Ethereum, USDC and USDT did not exhibit long-term patterns of high volatility, indicating they are low-risk digital assets. These results align with studies by Lyons and Viswanath-Natraj [34] and Foley et al. [35], who found that stablecoins help maintain stability in cryptocurrency systems, particularly during periods of liquidity scarcity. Notably, the study also reveals a split among digital assets: while speculative tokens like Bitcoin and Ethereum offer high returns but behave like risky assets, stablecoins function more like money market alternatives, providing reliability and low volatility. This difference suggests that discussions about digital assets should treat speculative cryptocurrencies and stablecoins separately, rather than treating all digital assets uniformly. These findings have broader implications for individuals who invest money and for those who create financial plans. For investors, the results indicate that the benefits of diversifying investments into cryptocurrencies depend on the timing. Bitcoin and Ethereum may help protect a portfolio during significant financial crises by offering some protection. Still, in regular market conditions, they behave more like risky investments, which means they aren't very good at being safe options. Stablecoins, however, maintain their value stability and offer good liquidity, making them a reliable choice for those who want to avoid risk or manage cash in their investment plans. For individuals who create financial policies, the results demonstrate the importance of stablecoins within the economic system. Their ability to remain stable even during challenging market times demonstrates their value in financial progress. Still, it also raises concerns about the amount of money kept in reserve, the transparency of their operations, and how to manage risks that could impact the Financial Stability Board (FSB) [36] and the IMF [37] emphasize the need for rules that address both the benefits and risks associated with stablecoins. Overall, the discussion presents a complex picture: cryptocurrencies aren't as dependable as gold or government bonds when it comes to being safe investments, although they may help diversify a portfolio during major crises. Stablecoins, on the other hand, have become the most steady and reliable digital tools for protecting against risks and keeping money liquid. This highlights the importance of clearly distinguishing between different types of digital assets in both research and when developing financial policies. 5. Conclusion and Recommendations 5.1 Conclusion This study examined how digital assets diversified and served as safe havens during two distinct periods: the COVID-19 crisis (2020–2021) and the post-crisis period (2022–2023). We employed simple statistics, correlation checks, and GARCH models to examine the behavior of Bitcoin, Ethereum, and two primary stablecoins (USDC and USDT) in comparison to traditional financial assets, including stocks, gold, government bonds, and economic indicators. The results revealed a distinct difference between speculative cryptocurrencies and stablecoins. During the crisis, Bitcoin and Ethereum didn’t really act as safe havens. They had weak or negative links with stocks and some connection with gold and bonds, which is similar to what Bouri et al. [ 38 ] found. However, in the aftermath of the crisis, these cryptocurrencies became increasingly linked to riskier markets, rendering them less of a safe option and more akin to speculative investments. Stablecoins, on the other hand, consistently demonstrated stability and helped preserve value throughout the entire period. Their price didn’t fluctuate significantly, except for brief periods in 2021, so they acted more like low-risk money market tools, which aligns with what Aramonte et al. [ 39 ] and Huynh et al. [ 40 ] stated. These differences demonstrate that the digital asset world is uniform. Cryptocurrencies may offer high returns and some assistance in times of significant shocks. Still, stablecoins are more reliable and can support risk management, as well as maintain money flow in mixed investment setups. The key point is that policymakers, regulators, and large investors should not view all digital assets uniformly. They should distinguish between speculative tokens, which are becoming increasingly financial and risky, and stablecoins, which are becoming key tools for facilitating money movement in both regular and decentralized finance. This distinction is crucial for creating rules, planning investments, and managing risks as digital finance continues to evolve. Ultimately, the study concludes that while digital assets cannot rival gold or government bonds as safe havens for money, stablecoins have the potential to enhance portfolio stability and support financial resilience across various market conditions. 5.2 Theory Contribution and Practical Implications: This study makes a significant contribution to the literature on digital assets and financial stability in three key ways. First, it provides one of the earliest long-horizon analyses of safe-haven dynamics, covering both the COVID-19 crisis (2020–2021) and the post-COVID recovery (2022–2023). This dual-phase approach surpasses earlier crisis-only studies, offering a more comprehensive perspective on asset behavior across various market regimes. Second, the study distinguishes between volatile cryptocurrencies (Bitcoin and Ethereum) and relatively stable digital assets (USDC and USDT), demonstrating that these categories exhibit fundamentally different volatility profiles, correlations, and safe-haven properties. This differentiation addresses a gap in prior research, which often treated digital assets as a homogeneous class. Third, the study connects empirical evidence to global regulatory debates, including frameworks advanced by the IMF, FSB, and the EU’s MiCA regulation. This linkage highlights the systemic implications of digital assets and bridges financial econometrics with policy design. From a practical perspective, the results offer clear guidance for investors, portfolio managers, and policymakers. For investors, Bitcoin and Ethereum may serve as opportunistic diversifiers during periods of extreme stress, but their volatility makes them unreliable as long-term safe havens. Stablecoins—particularly USDC—proved to be effective liquidity-preserving instruments, suitable for short-term hedging and portfolio risk management. For policymakers, the evidence underscores the importance of integrating stablecoins into financial stability frameworks with emphasis on transparency, reserve adequacy, and systemic oversight. Ultimately, the study demonstrates that digital assets cannot be uniformly classified as safe havens; instead, they play differentiated roles that present both opportunities and challenges for building resilient financial systems in the post-pandemic era. 5.3 Recommendations Based on the study's findings, several suggestions are offered for investors, regulators, and policymakers seeking to utilize digital assets in a manner that leverages their benefits while mitigating the associated risks. For investors, the results indicate that distinct strategies are required when incorporating digital assets into investment portfolios. Bitcoin and Ethereum should be viewed as high-risk, speculative investments. They might help diversify a portfolio during times of big market shocks, but they don’t reliably act as safe investments during calm periods. Because of this, portfolio managers are advised to keep their exposure to these assets within limits that are commensurate with their risk level. They should also pair these assets with more traditional methods of protecting against losses, such as gold or government bonds [ 41 ]. On the other hand, stablecoins like USDC and USDT offer a reliable way to keep money safe and can serve as an alternative to short-term cash management tools. Especially for large institutions, these stablecoins can be particularly helpful during financial crises, as they prevent significant drops in portfolio value. For regulators and policymakers, the evidence suggests that it's crucial to establish robust and transparent rules for stablecoins. Although USDC and USDT have been pretty stable over most of the time studied, there were brief periods in 2021 where their volatility spiked. These events highlight weaknesses in how reserves are managed and the level of confidence the market has in them. Groups like the Financial Stability Board and the Bank for International Settlements have already discussed the significant implications of stablecoins [ 42 ], and our research supports the need for increased supervision, greater transparency regarding reserves, and clear rules to prevent risks from spreading. Regulators should also treat speculative cryptocurrencies and stablecoins differently in their regulations, as they serve distinct roles in the financial system. For future policies, a balanced approach is suggested. Instead of imposing strict limits on all digital assets, authorities should support innovation in the stablecoin sector while ensuring the system remains secure. This includes facilitating the integration of stablecoins with central bank digital currencies (CBDCs) and ensuring adherence to anti-money laundering (AML) and know-your-customer (KYC) regulations [ 43 ]. This approach would help strengthen digital finance systems and enable them to be safely integrated into regular financial markets. 5.3 Limitations and Future Research Directions This study provides valuable insights into how digital assets diversify and serve as safe havens; however, several factors must be taken into consideration. First, we examined four primary digital assets—Bitcoin, Ethereum, USDC, and USDT—over the period from 2020 to 2023. While this covers the crisis and post-COVID trends well, it overlooks other cryptocurrencies and stablecoins that may have different risks and returns. Future research could include more tokens, such as Binance Coin, Ripple, or algorithmic stablecoins, to gain a better understanding of the entire digital asset world [ 44 ]. Second, the study primarily employed GARCH models to examine volatility. These models work well for financial data, but they might not show all the complex connections or changes in digital asset markets. Using more advanced methods, such as multivariate GARCH, copula models, or machine learning, for predicting volatility can provide more profound insights into how assets interact with each other and how they manage extreme risks [ 45 ]. Additionally, the study didn’t examine high-frequency data, which could provide more detailed insights into how assets interact during extremely stressful market periods. Third, the study adopts a global perspective but does not specifically examine regional differences in how people invest, the rules governing these investments, or the extent to which these assets are utilized in various areas. Digital markets are influenced by numerous factors in multiple locations, ranging from supportive laws in Switzerland and Singapore to stringent regulations in China. Future work should compare different countries to examine how regulations and market development impact the safety of digital assets [ 46 ]. Lastly, while this study yields essential results, digital finance is evolving rapidly, and new risks and opportunities are emerging that are not currently captured in the existing data. Future studies could explore the interaction between digital assets and central bank digital currencies (CBDCs), the functioning of decentralized finance (DeFi), and the risks associated with the widespread use of stablecoins [ 47 ]. These areas would help improve knowledge and guide both policy and investor decision-making in a more digital financial system. Declarations Ethics approval This study did not involve human participants or animals; therefore, ethics approval was not required. Competing interests The author declares no competing interests. Funding No funding was received to assist with the preparation of this manuscript. Author Contribution The sole author confirms that they were responsible for the conception, design, data collection, analysis, interpretation, and writing of the manuscript "*" Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods” Acknowledgement No funding was received to assist with the preparation of this manuscript. Data Availability The datasets generated and/or analyzed during the current study are available from the author on reasonable request. References Baur, D.G., & Lucey, B.M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review , 45(2), 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x Baur, D.G., & McDermott, T.K. (2010). Is gold a safe haven? International evidence. 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Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data , 31–43. https://doi.org/10.1016/B978-0-12-802117-0.00002-3 Ji, Q., Bouri, E., Lau, C.K.M., & Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63 , 257–272. https://doi.org/10.1016/j.irfa.2018.12.002 Zeng, T., Yang, M., & He, F. (2020). Daily return volatility forecasting of cryptocurrencies by a mixed data sampling model. Physica A: Statistical Mechanics and its Applications, 540 , 122868. https://doi.org/10.1016/j.physa.2019.122868 Schär, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 103 (2), 153–174. https://doi.org/10.20955/r.103.153-74 Additional Declarations No competing interests reported. Supplementary Files APPENDIXA.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor invited by journal 04 Feb, 2026 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 28 Oct, 2025 First submitted to journal 28 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7808569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":589263012,"identity":"17ab94ad-e4a6-4a1d-b83e-b7f517c29eff","order_by":0,"name":"Imran Hussain Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACNhDB2MBgYACkDwCxDD9IJKGABC08kg0gLQYErIJpAQEeA5BGBjxa+MTOPvxcuOOwsTn72YcHGNtseIzPr0788MCAQZ5f7AB2h0mnG0vPPHPYzLIn3QCoJY3H7MbbzRJAhxnOnJ2AQ0sagzRv22EbgwNpDEAth4Fazm4AaUkwuI1TC/NvsJbzzyBajGec3fyDgBY2kC1mBjegthjw924jZAubNe+ZdGODG0BbEs6l8Ujc4N1mkWAggdMv8rPTmG/z7rA23HA+jfnDhzIbOf7+s5tv/qiwkeeXxq4FFYDVSEBIIpTDAf8BUlSPglEwCkbBCAAAIOhbD71psIIAAAAASUVORK5CYII=","orcid":"","institution":"University of Lahore","correspondingAuthor":true,"prefix":"","firstName":"Imran","middleName":"Hussain","lastName":"Shah","suffix":""}],"badges":[],"createdAt":"2025-10-08 13:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7808569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7808569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102651673,"identity":"0d058a4b-2bd0-4720-98d1-4fe492194fd8","added_by":"auto","created_at":"2026-02-14 06:58:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":181250,"visible":true,"origin":"","legend":"\u003cp\u003eThe GARCH volatility graph for Bitcoin log returns highlights pronounced fluctuations across the COVID and post-COVID periods. During 2020 and 2021, conditional variance remained relatively elevated but stable, with peaks approaching 0.036, reflecting persistent volatility clustering during the global crisis. Volatility intensified in late 2021, coinciding with sharp swings in cryptocurrency markets, before gradually moderating in early 2022. However, from 2022 onward, the graph shows greater instability with sharp drops and spikes, particularly in 2023, where volatility repeatedly fell close to zero before rebounding abruptly. This pattern suggests that while Bitcoin’s volatility persistence remained high during the crisis years, the post-COVID phase was marked by sudden and irregular shocks, consistent with speculative trading and reduced haven capacity. Overall, the figure reinforces the statistical findings that Bitcoin remained a highly volatile asset throughout, offering limited protection in stable periods but reacting strongly during times of stress.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7808569/v1/b455d5c0e3689e5f2d49ec16.jpeg"},{"id":102747481,"identity":"80016874-53d0-4e6e-97ad-575dd98e32c6","added_by":"auto","created_at":"2026-02-16 09:04:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198817,"visible":true,"origin":"","legend":"\u003cp\u003eThe GARCH volatility plot for Ethereum log returns exhibits a sharp structural shift between the COVID crisis and the post-COVID period. In early 2020, Ethereum’s conditional variance was elevated, fluctuating between 0.03 and 0.04, and peaked above 0.045 during episodes of market turmoil, highlighting its speculative and high-risk profile. However, by late 2020 and early 2021, volatility dropped dramatically to around 0.015, marking a significant stabilization compared to Bitcoin. From 2021 to 2022, volatility remained subdued, oscillating between 0.005 and 0.015, suggesting a reduced sensitivity to external shocks as the Ethereum market matured. Interestingly, by 2023, volatility began to climb gradually, reaching values above 0.015, possibly reflecting renewed speculation and macro-financial uncertainty. Overall, the figure demonstrates that while Ethereum was initially more volatile than Bitcoin, it stabilized more quickly during the recovery phase. However, it continued to exhibit cyclical fluctuations, confirming its evolving but still speculative nature.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7808569/v1/9a7964fb6ce44dfe0865aacf.jpeg"},{"id":102651676,"identity":"ccef8537-1119-4d54-9966-60e2d3eb7002","added_by":"auto","created_at":"2026-02-14 06:58:36","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171726,"visible":true,"origin":"","legend":"\u003cp\u003eThe GARCH volatility plot for USDC log returns underscores the stablecoin’s role as a low-risk digital asset. Throughout the 2020–2023 period, conditional variance remained close to zero, reflecting the effectiveness of its peg to the US dollar. The only notable deviation occurred in the second quarter of 2021, when volatility spiked sharply to around 0.015, likely driven by temporary market dislocations or liquidity pressures in stablecoin markets. However, this spike was short-lived, and volatility quickly reverted to near-zero levels, highlighting USDC’s ability to absorb shocks and maintain stability. Unlike Bitcoin and Ethereum, USDC showed no evidence of persistent volatility clustering, further validating its use as a safe liquidity-preserving instrument.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7808569/v1/7a353a02347f7a49abfce220.jpeg"},{"id":102651677,"identity":"303d6561-e7cb-43b8-aecd-e3d8ce157d5c","added_by":"auto","created_at":"2026-02-14 06:58:37","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":200808,"visible":true,"origin":"","legend":"\u003cp\u003eThe GARCH volatility plot for USDT log returns reveals its general stability, with one notable exception of disruption. Similar to USDC, conditional variance remained near zero for most of the 2020–2023 period, reflecting its dollar-pegged design. However, in the second quarter of 2021, USDT experienced an extraordinary volatility spike reaching almost 0.09, far higher than the temporary spike observed in USDC. This indicates heightened stress in the stablecoin market, possibly related to liquidity mismatches or regulatory uncertainty surrounding Tether’s reserve backing. Following this episode, volatility immediately collapsed back to negligible levels and remained stable throughout 2022 and 2023, underscoring the resilience of the peg once market pressures eased. Unlike Bitcoin and Ethereum, which exhibited persistent volatility clustering, USDT maintained long-run stability despite temporary stress shocks. Overall, the figure confirms that USDT, while vulnerable to rare systemic events, consistently delivered stability post-crisis, reinforcing its role as a liquidity-preserving digital asset in diversified portfolios.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7808569/v1/7d7736ef0a4fe09fc8fb232c.jpeg"},{"id":102750716,"identity":"32a303a0-363d-4719-9557-53fcda13a674","added_by":"auto","created_at":"2026-02-16 09:21:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2379979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7808569/v1/44a33da9-61ff-4984-a6d9-554e30828257.pdf"},{"id":102651675,"identity":"ab1c3e62-0ea2-4149-b505-b336614336dc","added_by":"auto","created_at":"2026-02-14 06:58:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29119,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIXA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7808569/v1/e58acbd0fedf3e52f26329f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cstrong\u003e1.1 Introduction \u0026amp; Background\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe COVID-19 pandemic led to significant changes in global markets, prompting a rapid increase in the use of digital assets. This raised questions about whether these assets could act as safe places for investors during uncertain times. Earlier studies examined Bitcoin and Ethereum during the crisis years (2020\u0026ndash;2021), but most didn\u0026rsquo;t investigate what happened after the pandemic ended and new rules began to be discussed. This paper fills that gap by examining how Bitcoin, Ethereum, and two main stablecoins (USDC and USDT) performed during both the crisis and recovery periods (2020\u0026ndash;2023). It employs a specific model, known as the GARCH (1,1) model, to analyze its behavior. The results show that cryptocurrencies offered only a small benefit for diversifying a portfolio, as they remained highly volatile. However, stablecoins, especially USDC, acted as safer investments with less risk. By examining the time period beyond the crisis and connecting these findings to key discussions in global policy, such as those from the IMF, FSB, and the EU\u0026rsquo;s MiCA framework, this study demonstrates how digital assets can serve two roles: helping to diversify portfolios and also influencing financial regulations. The unique aspect of this study is that it provides a long-term, comparative perspective, linking the volatility of these assets to the development of economic stability policies in the aftermath of the pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Research Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExisting research gives unclear answers about whether digital assets can serve as safe havens. Some studies suggest that Bitcoin may act as a hedge during significant market downturns, while other studies indicate that it behaves more like a high-risk investment, closely tied to stock prices. Ethereum has not been extensively studied in this area, despite its rapid growth and widespread adoption. Stablecoins, which are commonly used for trading and providing liquidity, have not been much studied as possible safe havens either. Additionally, most studies focus solely on the period during and immediately after the COVID-19 crisis, without examining whether digital assets continue to exhibit haven qualities when market swings return to normal. There\u0026apos;s a big gap in research when it comes to comparing different types of digital assets (like cryptocurrencies and stablecoins) and looking at different market periods (like crisis and recovery).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Problem Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough digital assets are receiving increasing attention in both academic studies and real-world discussions, considerable uncertainty remains about whether they can serve as safe investments during significant financial crises. Cryptocurrencies are highly unpredictable and often perceived as a risky investment, yet they are still referred to as \u0026quot;digital gold.\u0026quot; Stablecoins are designed to maintain their value stability. Still, it\u0026apos;s unclear how well they can withstand sudden market changes and whether they contribute to the overall stability of the financial system. This issue is exacerbated by the lack of research comparing how these assets perform under different financial scenarios. If these gaps aren\u0026apos;t filled, investors may invest in digital assets based on incorrect assumptions about their safety, and regulators may not fully understand the broader implications of using stablecoins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1times of financial uncertainty\u003c/strong\u003e study seeks to address the following central research question:\u003c/p\u003e\n\u003cp\u003eDo digital assets\u0026mdash;specifically Bitcoin, Ethereum, USDC, and USDT\u0026mdash;serve as safe havens and provide portfolio diversification benefits during periods of financial stress, such as the COVID-19 crisis, and how does their role change in the post-COVID recovery period?\u003c/p\u003e\n\u003cp\u003eFrom this central question, several sub-questions emerge:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eHow do the volatility and persistence of digital assets differ across the COVID and post-COVID periods?\u003c/li\u003e\n \u003cli\u003eDo stablecoins demonstrate stronger haven characteristics than cryptocurrencies?\u003c/li\u003e\n \u003cli\u003eHow do correlations between digital assets and traditional haven instruments (e.g., gold, Treasuries) evolve during crisis and recovery?\u003c/li\u003e\n \u003cli\u003eWhat implications do these findings carry for investors, portfolio managers, and regulators?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Research Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe objectives of this study are fourfold:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eTo analyze the volatility behavior of Bitcoin, Ethereum, USDC, and USDT during the COVID-19 crisis and the post-COVID recovery period, focusing on volatility clustering and persistence using GARCH-type models.\u003c/li\u003e\n \u003cli\u003eTo examine the correlation dynamics between digital assets and traditional financial instruments to assess their hedging and diversification potential.\u003c/li\u003e\n \u003cli\u003eTo compare the haven properties of cryptocurrencies versus stablecoins, highlighting their differences in risk and stability.\u003c/li\u003e\n \u003cli\u003eTo draw practical and policy-relevant implications for investors, regulators, and policymakers regarding the inclusion of digital assets in portfolios and financial stability frameworks.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2. Literature Review ","content":"\u003cp\u003eSafe-haven assets have always been important in financial economics, particularly when market uncertainty or stress is high. Investors typically turn to assets such as gold, U.S. Treasury securities, and other low-risk investments to safeguard their wealth during challenging financial times. However, with the rise of digital assets such as Bitcoin and Ethereum, as well as stablecoins like USDC and USDT, people are wondering if these can also serve as safe havens. The start of the COVID-19 pandemic in early 2020 created a significant global shock, providing researchers with an opportunity to test whether digital assets can serve as safe havens during both crisis and recovery periods. Some studies suggest that Bitcoin and Ethereum can help mitigate risk in extreme situations. Still, others highlight their high volatility and tendency to move in tandem with other high-risk assets. On the other hand, stablecoins are designed to maintain a stable value by maintaining a fixed relationship with fiat currencies, making them more reliable. However, there isn\u0026rsquo;t much real-world evidence yet about how they perform during tough times.\u003c/p\u003e\n\u003ch3\u003e2.1 Safe Haven Theory and Portfolio Diversification\u003c/h3\u003e\n\u003cp\u003eBaur and Lucey [1] discuss two types of assets: hedge assets, which don\u0026apos;t move significantly with stocks, and safe-haven assets, which act as a shelter during turbulent times. Gold is often seen as the classic haven across different markets [2]. U.S. Treasuries also serve as a hedge because they typically move in the opposite direction of riskier investments during adverse market conditions [6]. Markowitz [7] explained that diversifying a portfolio with assets that have low or negative correlations helps reduce overall risk. Erb and Harvey [8] emphasize the importance of commodities in a strategy. At the same time, Ang and Bekaert [9] suggest using models that adapt to different market situations to better understand how correlations shift during crises.\u003c/p\u003e\n\u003cp\u003eRecently, cryptocurrencies have raised questions about the effectiveness of diversification. Dyhrberg [10] studied Bitcoin using GARCH models and found it has some similarities with gold and the U.S. dollar, suggesting it might act like a haven. However, because Bitcoin is highly volatile, it\u0026apos;s unclear whether it can reliably serve as a hedge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Cryptocurrencies and Volatility Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBitcoin\u0026apos;s price fluctuations have been closely examined, often indicating speculative bubbles and periods of high volatility. Cheah and Fry [3] viewed Bitcoin as an asset that tends to form bubbles, whereas Katsiampa [11] found that volatility in Bitcoin is persistent, utilizing models such as GARCH. Corbet et al. [12] demonstrated that cryptocurrencies are not distinct from other financial markets, closely tracking stocks, commodities, and currencies. Ethereum, the second-biggest cryptocurrency, has not been studied as much, but it has its own unique features.\u003c/p\u003e\n\u003cp\u003eLiu and Tsyvinski [13] examined the risks and returns of digital assets, finding that Ethereum is more closely tied to stock markets than Bitcoin. Klein et al. [14] said that neither Bitcoin nor Ethereum is as stable as gold, which weakens their claim of being a safe investment during tough times. Urquhart [15] also noted that inefficiencies in cryptocurrency markets make them more speculative.\u003c/p\u003e\n\u003cp\u003eThe COVID-19 crisis provided a real-life test of these market behaviors. Conlon et al. [4] suggested that Bitcoin did not act as a safe investment in March 2020 and instead increased the risk in portfolios. Goodell and Goutte [16] employed wavelet coherence analysis to demonstrate that Bitcoin\u0026apos;s movement was correlated with the number of COVID-19 cases, suggesting that volatility was driven by public sentiment rather than being a safe asset. These results highlight the limitations of relying on cryptocurrencies as a reliable source of protection during major crises.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Stablecoins and Financial Stability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStablecoins like USDC and USDT differ from regular cryptocurrencies because their values are tied to a real currency, such as the US dollar. This link helps keep their prices steady and less likely to fluctuate significantly [17]. Studies show that stablecoins are essential in the crypto world because they help keep markets running smoothly by providing liquidity [18]. However, questions remain about the clarity of their financial backing and whether they are truly backed by real money [19]. Arner and others [20] warn that if stablecoins proliferate without proper rules, it could create significant problems for the financial system.\u003c/p\u003e\n\u003cp\u003eIn times of trouble, stablecoins are intended to be a safer choice, allowing people to move money out of risky investments without relying on traditional banks. Lyons and Viswanath-Natraj [21] found that when things get tough, people tend to use stablecoins more, indicating a preference for stability. However, USDT has been questioned about whether it has sufficient real-world value to back its price, which has caused some distrust in the market [22]. On the other hand, USDC is perceived as more transparent and trustworthy, making it a better choice for individuals seeking a secure alternative [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Digital Assets During COVID-19\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe COVID-19 pandemic presented researchers with a real-world opportunity to test digital assets as tools for mitigating financial risks. Studies show that cryptocurrencies behave differently. One study found that Bitcoin posed more risk than a safe investment [24], while another showed that digital assets and stock markets became increasingly connected during the pandemic. However, another study highlighted that in certain areas, digital assets offer benefits by spreading out risks [25], indicating that their performance varies depending on the region. Stablecoins, which are less studied, showed more strength.\u003c/p\u003e\n\u003cp\u003eResearch has found that stablecoins maintained their value during the significant market drop in March 2020, which supports their use as a substitute for cash. But events like the temporary loss of value in USDT show that they are not without problems. Since stablecoins are not well-studied in academic research, there is a lack of understanding about their role in financial systems during periods of economic stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Post-COVID Shifts in Digital Assets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the pandemic, the economy underwent significant changes, driven by rising prices, tighter monetary policies, and market adjustments. Research indicates that, after 2021, the relationship between cryptocurrencies and traditional investments weakened [26], suggesting that cryptocurrencies may offer more effective ways to diversify risk. Ethereum, in particular, has exhibited less volatility over time, indicating that the market is becoming more stable [27]. However, cryptocurrencies still exhibit many speculative traits, so they do not yet fully act as safe investments.\u003c/p\u003e\n\u003cp\u003eStablecoins, however, have become more trusted as reliable tools. USDC, for example, is now widely used for making payments and settling transactions [28]. New rules from organizations such as the European Union and the U.S. have underscored the importance of stablecoins for the overall financial system [29]. These changes suggest that while cryptocurrencies may continue to be used for diversification, stablecoins could eventually be viewed as reliable safe havens if properly managed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Research Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research suggests that three primary areas require additional attention. First, most studies focus on Bitcoin, but they pay little attention to Ethereum, and even less to how cryptocurrencies compare with stablecoins within a single, clear framework. Second, much of the work focuses solely on the time during the COVID-19 crisis, and it overlooks what happens after the crisis, which may alter how people perceive these assets as safe havens. Third, the methods employed are inconsistent\u0026mdash;some use simple correlations, while others employ complex econometric models, resulting in mixed findings. This study fills these gaps by employing basic statistics, correlation analysis, and GARCH models to examine Bitcoin, Ethereum, USDC, and USDT during both the pandemic and its aftermath, providing a more comprehensive view that helps both scholars and practitioners gain a better understanding.\u003c/p\u003e\n\u003cp\u003eUnlike most earlier studies, which examined only the COVID-19 crisis period, our research provides a two-phase comparative analysis\u0026mdash;covering both the crisis (2020\u0026ndash;2021) and the post-COVID recovery period \u003cstrong\u003e(2022\u0026ndash;2023)\u003c/strong\u003e\u0026mdash;to identify how the safe-haven and diversification roles of digital assets evolved over time. Previous works, such as \u003cstrong\u003eConlon et al. (2020)\u003c/strong\u003e and \u003cstrong\u003eGoodell and Goutte (2021)\u003c/strong\u003e, focused primarily on the pandemic shock and found that Bitcoin failed to act as a haven, often amplifying portfolio risk. Similarly, \u003cstrong\u003eBouri et al. (2017)\u003c/strong\u003e and \u003cstrong\u003eDyhrberg (2016)\u003c/strong\u003e examined Bitcoin in isolation, without differentiating between asset classes or subsequent market phases. In contrast, our paper introduces a \u003cstrong\u003ecomparative framework\u003c/strong\u003e that evaluates not only cryptocurrencies (such as Bitcoin and Ethereum) but also stablecoins (including USDC and USDT)\u0026mdash;an area that has been largely overlooked in existing literature. Furthermore, previous research rarely linked empirical volatility evidence to \u003cstrong\u003epolicy and regulatory debates\u003c/strong\u003e. In contrast, our study integrates findings with global frameworks, including the \u003cstrong\u003eIMF\u0026rsquo;s digital finance guidelines, the FSB\u0026rsquo;s systemic risk directives, and the EU\u0026rsquo;s MiCA regulation\u003c/strong\u003e. Hence, while earlier studies treated digital assets as a single category and focused on short-term crisis reactions, this study offers a \u003cstrong\u003elong-horizon, cross-asset, and policy-connected approach\u003c/strong\u003e, advancing both academic and practical understanding of digital assets in the context of post-pandemic financial resilience.\u003c/p\u003e"},{"header":"3. Methods and Data","content":"\u003cp\u003e\u003cstrong\u003e3.1 Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employs a quantitative research method and applies econometric models to examine how digital assets serve as a haven and provide diversification benefits during two distinct market periods: the COVID-19 crisis from January 2020 to December 2021, and the recovery period from January 2022 to December 2023. Choosing a quantitative approach involves considering previous research on how assets are priced and how volatility is modeled, which focuses on identifying statistical connections between asset returns and broader economic and financial factors. The study employs tools such as descriptive statistics, correlation tables, and GARCH-type models to investigate the relationship between digital assets and traditional safe-haven assets, as well as overall market uncertainty. By examining both the crisis and recovery periods, the study provides a broader perspective, helping to overcome the limitation of earlier studies that focused solely on short-term crises.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Data Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset includes monthly data from January 2020 to December 2023. This period is divided into two parts: the time during the COVID-19 crisis (January 2020 to December 2021) and the time after the crisis (January 2022 to December 2023). Four digital assets are chosen: Bitcoin (BTC) and Ethereum (ETH) as examples of cryptocurrencies, and USDC and USDT as the most commonly used stablecoins. Including these options allows for a comparison between more speculative assets and those that aim to maintain stable value.\u003c/p\u003e\n\u003cp\u003eIn addition to these digital assets, the study also examines traditional safe-haven investments for comparison, including gold, U.S. Treasury yields, and the S\u0026amp;P 500 index.\u003c/p\u003e\n\u003cp\u003eThe analysis also includes macroeconomic and financial indicators, such as the U.S. inflation rate, interest rates, the U.S. dollar index, and the VIX (Volatility Index). These help in understanding overall risk and economic conditions. All price and index information is sourced from reputable and publicly accessible sources, including Yahoo Finance, Federal Reserve Economic Data (FRED), CoinMarketCap, and Investing.com. Monthly log returns are calculated as:\u003c/p\u003e\n\u003cp\u003eWhere PtP_tPt represents the closing price or index value at time t. This transformation ensures stationarity and reduces heteroskedasticity in the return series [4].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Variables and Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe variables are grouped into dependent and independent categories.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDependent Variables:\u003c/strong\u003e Log returns of digital assets (BTC, ETH, USDC, USDT). These capture the main dynamics under investigation, enabling the modeling of volatility and testing for safe havens.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIndependent Variables:\u003c/strong\u003e Log returns of benchmark assets and macro indicators.\u003cul type=\"circle\"\u003e\n \u003cli\u003e\u003cem\u003eGold returns\u003c/em\u003e \u0026ndash; proxy for traditional haven.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eS\u0026amp;P 500 returns\u003c/em\u003e \u0026ndash; proxy for equity market performance.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eTreasury yields\u003c/em\u003e \u0026ndash; benchmark for fixed-income safe havens.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eInflation (CPI)\u003c/em\u003e \u0026ndash; proxy for price stability.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ePolicy rates\u003c/em\u003e \u0026ndash; represent monetary tightening/loosening.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eU.S. Dollar Index\u003c/em\u003e \u0026ndash; measures currency strength.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eVIX\u003c/em\u003e \u0026ndash; captures global market uncertainty.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy including these variables, the models assess whether digital assets behave like safe havens (negatively correlated with risky assets during crises) or speculative assets (positively correlated with equities).\u003c/p\u003e\n\u003cp\u003eTo analyze volatility and persistence, the study employs GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, which are widely used in financial econometrics for modeling time-varying variance [5]. The GARCH (1,1) specification is given as:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rt = In (P\u003csub\u003et\u003c/sub\u003e/P\u003csub\u003e(t-1)\u003c/sub\u003e)\u003c/p\u003e\n\u003cp\u003ewhere PtP_tPt is the daily closing price at time ttt.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eIndependent Variables: Market volatility, macroeconomic shocks, and asset-specific risk factors serve as independent variables. For digital assets, volatility proxies are derived using GARCH (1,1), EGARCH, TGARCH, and GJR-GARCH models. For traditional safe-haven assets (gold, US Treasuries), historical volatility is used for comparison.\u003c/li\u003e\n \u003cli\u003eControl Variables: Inflation rate, policy rate changes, and global risk aversion indices (such as the VIX) are included to account for macro-financial influences.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll return series are tested for stationarity using Augmented Dickey-Fuller (ADF) and Phillips\u0026ndash;Perron (PP) unit root tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 1: Descriptive Statistics \u0026amp; Correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDescriptive measures (mean, variance, skewness, kurtosis, Jarque-Bera) provide insight into the statistical properties of returns.\u003c/li\u003e\n \u003cli\u003eCorrelation matrices compare co-movements of digital assets with traditional assets during the COVID and post-COVID periods.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eStep 2: GARCH Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eGARCH (1,1) is estimated separately for BTC, ETH, USDC, and USDT.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRBtci = \u0026mu; + \u0026beta;1rSP500t + \u0026beta;2rGoldt + B3rInflationt + B4rPRt + B5rTRt + B6rUSDIt + B7rVIXt\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDiagnostics (AIC, SC, log likelihood, Durbin-Watson) assess model fit.\u003c/li\u003e\n \u003cli\u003eStability is confirmed using ARCH-LM tests to ensure no residual autocorrelation in variance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe have tested four models. The Dependent Variables only changed BTC, ETH, USDC, and USDT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 3: Comparative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eCompare coefficients across COVID vs post-COVID.\u003c/li\u003e\n \u003cli\u003eAssess volatility clustering, persistence, and differences between cryptocurrencies and stablecoins.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e3.5 Justification of Methods\u003c/h3\u003e\n\u003cp\u003eThe GARCH framework is particularly suitable given the stylized facts of financial time series, including volatility clustering, fat tails, and heteroskedasticity. While alternatives such as EGARCH or TGARCH could account for asymmetry, the GARCH (1,1) remains the most parsimonious and widely adopted in studies of cryptocurrencies. Correlation analysis complements GARCH by offering a simple but powerful measure of diversification potential. Together, these methods provide both descriptive and dynamic insights.\u003c/p\u003e\n\u003cp\u003eThe decision to split the analysis into COVID and post-COVID periods enhances robustness, allowing direct comparison of asset behaviors under crisis and recovery conditions. This temporal segmentation aligns with prior research frameworks assessing haven dynamics across different phases of systemic stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe method used is thorough, but it has some limits. One issue is that using monthly data may miss sudden, large price swings that occur within a day or during the day. However, this choice was made because the data are available and the study spans an extended time period. Another limitation is that stablecoins are a relatively new concept, so there isn\u0026apos;t much long-term history to work with, which makes it harder to build a robust model. Additionally, GARCH models assume that events follow a regular pattern, which may not accurately account for extremely rare or unusual situations. Future work could employ more advanced methods, such as multivariate GARCH, copula models, or models that adapt to different market conditions. Lastly, although the study includes essential economic and financial factors, it can\u0026apos;t fully account for aspects that aren\u0026apos;t measured, such as people\u0026apos;s feelings about the market or the perceived trustworthiness of policies.\u003c/p\u003e"},{"header":"4. Empirical Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents descriptive statistics for Bitcoin, Ethereum, Gold, the S\u0026amp;P 500, Oil, and US Treasury Bonds. Bitcoin and Ethereum exhibit the highest mean returns but also the highest standard deviations, reflecting their volatile nature. Gold exhibits moderate average returns with low variance, consistent with its reputation as a haven. Skewness and kurtosis values indicate that Bitcoin and Ethereum have heavy-tailed distributions, confirming non-normality in their return series. Jarque\u0026ndash;Bera tests reject the null hypothesis of normal distribution for all digital assets, justifying the use of GARCH-family models.\u003c/p\u003e\n\u003cp\u003eTable 1 Appendix A\u003c/p\u003e\n\u003cp\u003eTable 1 presents the basic statistical information on digital assets and traditional financial tools during the COVID-19 crisis period (2020\u0026ndash;2021) and in the aftermath of the crisis (2022\u0026ndash;2023). During the crisis, Bitcoin (BTC) and Ethereum (ETH) had high average returns but also significant fluctuations in their values, indicating that they are highly volatile. Both of these assets exhibited a positive skew and high kurtosis, indicating the presence of extreme returns and heavy-tailed distributions. Gold (GLDLR), on the other hand, exhibited moderate positive returns with lower volatility, suggesting that it acts as a traditional safe asset. The equity indices (SPLR, MSCIELR, MSCIALR) exhibited high kurtosis and positive skew, which is typical during periods of financial stress, while the VIX index displayed very high variance, indicating high uncertainty among investors. In the post-COVID period, volatility across most assets decreased, as indicated by lower standard deviations and less kurtosis. Bitcoin and Ethereum still had positive returns, but with smaller swings, which suggests the market was becoming more stable. Traditional safe assets, such as gold and bonds, maintained their low-risk profiles, while equity indices recovered with less skewness and kurtosis. Overall, the results indicate that while digital assets generated good returns during the crisis, their risk levels were still significantly higher than those of traditional safe havens, which limited their ability to serve as reliable safe assets compared to gold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation matrices reveal that during the \u003cstrong\u003eCrisis Period\u003c/strong\u003e, Bitcoin\u0026rsquo;s correlation with equities (S\u0026amp;P 500) turned weakly positive but remained significantly lower than that of traditional assets. Its correlation with gold was near zero, suggesting potential benefits from diversification. In contrast, in the \u003cstrong\u003epost-COVID period\u003c/strong\u003e, Bitcoin\u0026rsquo;s correlation with equities strengthened, reducing its hedging potential, while gold retained its negative correlation with equities.\u003c/p\u003e\n\u003cp\u003eTable 2 Appendix A\u003c/p\u003e\n\u003cp\u003eTable 2 illustrates how Bitcoin\u0026apos;s connections with other investments evolved during two distinct periods: the COVID crisis and the subsequent recovery. During the crisis, which spanned from 2020 to 2021, Bitcoin exhibited weak to moderate positive correlations with stocks such as the S\u0026amp;P 500 (0.39) and MSCI Asia (0.48), indicating a mild connection to traditional markets. However, its link with gold was negative (-0.41), suggesting that it might have been a good choice to diversify risk. Bitcoin also had a moderate positive correlation with safe investments, such as Treasury bonds (0.41), indicating that it played both a speculative and a hedging role during uncertain times. In the period following the crisis, from 2022 to 2023, Bitcoin\u0026rsquo;s correlation with stocks increased significantly, as seen with the S\u0026amp;P 500 (0.52) and MSCI Europe (0.45), indicating that it became more closely tied to riskier investments and less of a haven. Its link with gold became much weaker, approaching neutrality (0.11), meaning it no longer acted like gold in protecting against losses. Interestingly, Bitcoin\u0026rsquo;s link with Ethereum increased significantly, from 0.20 to 0.60, indicating that in more stable times, the crypto market is becoming more interconnected within itself. Overall, the results suggest that Bitcoin exhibited some safe-haven qualities during the crisis. Still, during the post-COVID period, it became more closely linked with riskier assets, which weakens its ability to help diversify a portfolio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 GARCH Model Table 3 to 6\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99.6769%;\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3 GARCH Model 1\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 99.6769%;\"\u003e\n \u003cp\u003eDependent Variables: Bitcoin Log return\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99.6769%;\" colspan=\"5\"\u003e\n \u003cp\u003eCOVID Period Jan 2020 to Dec 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.208053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.069996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-2.97236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.644188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.466418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.38114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.1672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-31.08981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.82973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-2.628107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.0086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.957711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.292735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.740841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.4588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-2.4143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.442632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.988401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.15158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.081426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.861581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.0627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.636246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.317329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.005008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eVariance Equitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.010329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.013419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.769752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.4414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.187875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.146634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.281249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.2001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.587602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.094962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.536641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.5915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 560px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel diagnostics\u003c/strong\u003e: R\u0026sup2; = 0.506; Adj. R\u0026sup2; = 0.332; DW = 2.012;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 560px;\"\u003e\n \u003cp\u003eLog Likelihood = 18.92; AIC = \u0026ndash;0.749; SC = \u0026ndash;0.259; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePost COVID Period Jan 2022 to Dec 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;0.118704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.045934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026ndash;2.584198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.0098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.602586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.381015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.581528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.1138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;33.88604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e24.02921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026ndash;1.410222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.1585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;0.140090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.849057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026ndash;0.164994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.8689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.500011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.706086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.708136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.4454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.2427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.426563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.568967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.5694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;0.489463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.376334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026ndash;1.300607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.1934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 322px;\"\u003e\n \u003cp\u003eVariance Equitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.003163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.005946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.531916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.5948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026ndash;0.308945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.200466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026ndash;1.541178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 322px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.16719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.25614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.556844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 560px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = 0.256; Adj. R\u0026sup2; = \u0026ndash;0.072; DW = 2.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 560px;\"\u003e\n \u003cp\u003eLog Likelihood = 16.55; AIC = \u0026ndash;0.546; SC = \u0026ndash;0.126; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 presents the results from the GARCH model, highlighting significant changes in what affects Bitcoin\u0026rsquo;s returns before and after the COVID-19 crisis. During the crisis years (2020\u0026ndash;2021), returns on Treasury bonds had a strong positive effect on Bitcoin, indicating that Bitcoin\u0026apos;s value moved somewhat in line with safer government assets. However, changes in interest rates had only a weak effect. Other factors, such as stock prices (S\u0026amp;P 500), gold, and inflation, had no significant impact, indicating that Bitcoin was somewhat separate from traditional financial markets at that time. Examining the change in volatility, neither the ARCH nor GARCH terms were significant; however, the positive sign on GARCH (1) suggested some ongoing fluctuations.\u003c/p\u003e\n\u003cp\u003eIn the years following the crisis (2022\u0026ndash;2023), none of the factors had a significant impact on Bitcoin\u0026rsquo;s returns, suggesting that Bitcoin\u0026rsquo;s performance became more distinct and less tied to broader financial trends.\u003c/p\u003e\n\u003cp\u003eHowever, in the volatility part of the model, the GARCH (1) term was highly significant and positive, indicating that Bitcoin\u0026rsquo;s volatility exhibits strong persistence and long-term memory. Overall, these findings suggest that while Bitcoin was briefly linked to Treasury bonds during the crisis, after the crisis, it became more of a speculative asset driven by volatility, with returns less connected to traditional safe assets and overall economic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.1 GARCH Model 2 Table 4\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100%;\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4 GARCH Model 2\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 99.8442%;\"\u003e\n \u003cp\u003eDependent Variables: Ethereum Log return\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 99.8442%;\"\u003e\n \u003cp\u003eCOVID Period Jan 2020 to Dec 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;0.232571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.124868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;1.862544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2.338785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.994741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.351151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;28.98526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16.37508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;1.770084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.016038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.218599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.073324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.386511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.9645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.467572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.6401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.683548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.224113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.0482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.053088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.449093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.118122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUSD_INDEX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;1.380252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.053637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;0.340497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.7335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 250px;\"\u003e\n \u003cp\u003eVariance Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.014602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.03433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.42535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.6706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;0.242594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.368634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;0.658009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.5105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.650783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.356395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.826088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 456px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = 0.510; Adj. R\u0026sup2; = 0.297; DW = 2.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 456px;\"\u003e\n \u003cp\u003eLog Likelihood = 15.10; AIC = \u0026ndash;0.342; SC = 0.035; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 352px;\"\u003e\n \u003cp\u003ePost COVID Period Jan 2022 to Dec 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;0.027620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.04706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;0.588903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.5573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.67562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.627855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.076078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.2819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;19.43455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.6146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;1.673286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0943*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.105027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.221464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.474238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.6353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.750913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.710115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.193639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0283**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.364612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.712124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.512654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.6082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.396062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.419032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.945184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.3446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUSD_INDEX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.586296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.056451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e10.38587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.0003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 250px;\"\u003e\n \u003cp\u003eVariance Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.004792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.014901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.321614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.7477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ndash;0.159000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.707561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;0.224716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.8222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.617915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.474978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.418931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.6753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 456px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = 0.614; Adj. R\u0026sup2; = 0.445; DW = 1.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 456px;\"\u003e\n \u003cp\u003eLog Likelihood = 26.96; AIC = \u0026ndash;1.329; SC = \u0026ndash;0.790; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 shows that Ethereum has stronger and more flexible connections with traditional assets than Bitcoin. During the COVID crisis (2020\u0026ndash;2021), Ethereum\u0026apos;s returns were closely and positively linked to gold and US Treasury returns, meaning it behaved somewhat like traditional safe-haven assets. This is different from Bitcoin, which didn\u0026rsquo;t show strong safe-haven qualities during the same time. Also, the GARCH (1) term was positive and slightly significant, showing that Ethereum\u0026rsquo;s volatility tends to continue over time. In the time after the pandemic (2022\u0026ndash;2023), Ethereum\u0026apos;s behavior changed a lot: its returns became closely and positively connected to the S\u0026amp;P500, showing it\u0026apos;s becoming more linked to stock markets. At the same time, the US dollar index became a very important factor, showing Ethereum is sensitive to overall financial conditions. Inflation had a weak but noticeable effect, showing Ethereum is influenced by broader economic factors. In the part about volatility, neither the ARCH nor GARCH terms were significant, though the GARCH (1) had a positive coefficient, suggesting that volatility patterns still cluster. Overall, these results show that Ethereum acted as a transitional asset\u0026mdash;showing safe-haven traits during the crisis because of its connections with gold and Treasuries, but then becoming more like a risk-on asset after the crisis, as it became more connected to stocks and global economic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.2 GARCH Model 3 Table 5\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99.8489%;\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5 GARCH Model 3\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 99.8489%;\"\u003e\n \u003cp\u003eDependent Variables: USDC Log return\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 99.8489%;\"\u003e\n \u003cp\u003eCOVID Period Jan 2020 to Dec 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.004964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.003586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;1.384403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.1662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.035947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.039404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.912271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.3616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.960136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.36027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;2.665043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.002457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.012999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.188974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.8501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.113675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.51199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.6087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.033749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.011259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.919814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eUSD_INDEX_LOGRETURN \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.060865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.093686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.649670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.5159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.018849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.014765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.276566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 292px;\"\u003e\n \u003cp\u003eVariance Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3.86E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e5.69E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.677783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.687282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.842618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.002427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.014900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.025205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.591130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.5544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 581px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = \u0026ndash;0.042; Adj. R\u0026sup2; = \u0026ndash;0.498; DW = 3.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 581px;\"\u003e\n \u003cp\u003eLog Likelihood = 83.30; AIC = \u0026ndash;6.025; SC = \u0026ndash;5.485; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 292px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 397px;\"\u003e\n \u003cp\u003ePost COVID Period Jan 2022 to Dec 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;2.47E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3.31E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.000119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.001355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.022636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.020003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;1.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;1.10E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.000234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.000623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.000962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.000176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.000166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eUSD_INDEX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.000679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.003632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.001248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.000564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;2.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.026**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 292px;\"\u003e\n \u003cp\u003eVariance Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2.12E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2.97E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.991514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.199109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 292px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ndash;0.316327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.518954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026ndash;0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 581px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = 0.227; Adj. R\u0026sup2; = \u0026ndash;0.116; DW = 2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 581px;\"\u003e\n \u003cp\u003eLog Likelihood = 15.09; AIC = \u0026ndash;1.007; SC = \u0026ndash;0.526; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 The GARCH results for USDC, a stablecoin, confirm its role as a low-volatility asset with minimal dependence on traditional market drivers. During the COVID-19 crisis (2020\u0026ndash;2021), most explanatory variables were insignificant, except inflation (negative and significant) and Treasuries (positive and essential), suggesting that the USDC marginally reflected macroeconomic conditions and government bond dynamics. The variance equation reveals a significant ARCH term, indicating short-run volatility clustering, although the model\u0026apos;s overall explanatory power was weak (R\u0026sup2; = \u0026ndash;0.042). In the post-COVID period (2022\u0026ndash;2023), USDC returns remained unresponsive primarily to gold, equities, Treasuries, and policy rates, consistent with its peg to the US dollar. The only significant driver was the VIX, which showed a negative relationship, implying that USDC retained demand during periods of financial uncertainty. In the variance equation, neither the ARCH nor the GARCH terms were significant, confirming that volatility persistence was absent. Collectively, these results reinforce the stablecoin\u0026rsquo;s design: USDC behaves as a \u003cstrong\u003equasi-risk-free asset\u003c/strong\u003e with negligible exposure to traditional market movements, making it an effective tool for liquidity preservation and short-term hedging during both crisis and recovery phases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.3 GARCH Model 4 Table 6\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"685\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99.854%;\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6 GARCH Model\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 99.708%;\"\u003e\n \u003cp\u003eDependent Variables: USDT Log return\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99.708%;\" colspan=\"5\"\u003e\n \u003cp\u003eCOVID Period Jan 2020 to Dec 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.000268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.000299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.030968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.000617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;50.22396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.187085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.002318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;80.71322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.001342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.001137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;1.180633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.065128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.015453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4.177236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.002637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.000482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;5.457318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eUSD_INDEX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.065332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.000472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;138.4135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.004183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.001436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2.914266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 374px;\"\u003e\n \u003cp\u003eVariance Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;3.52E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.67E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.007534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eRESID(-1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42.28426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.78787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e3.306592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eGARCH(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.000452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.008921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.050657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 545px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = \u0026ndash;0.066; Adj. R\u0026sup2; = \u0026ndash;0.533; DW = 2.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 545px;\"\u003e\n \u003cp\u003eLog Likelihood = 18.32; AIC = \u0026ndash;0.652; SC = \u0026ndash;0.247; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 460px;\"\u003e\n \u003cp\u003ePost COVID Period Jan 2022 to Dec 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;5.66E\u0026ndash;05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.000203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eGOLD_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.003178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eINFLATION_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.073135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.041324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;1.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003ePOLICY_RATES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.000278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.000289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eS_P_500_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;3.83E\u0026ndash;06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.005929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eTREASURIES_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.003005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.025**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eUSD_INDEX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.017717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.013177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eVIX_LOGRETURN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.001326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 374px;\"\u003e\n \u003cp\u003eVariance Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003ez-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.16E\u0026ndash;08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e8.48E\u0026ndash;08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eRESID(\u0026ndash;1)^2 (ARCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;0.154777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.067353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026ndash;2.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.026**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003eGARCH(\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.621473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.795128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 374px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Diagnostics:\u003c/strong\u003e R\u0026sup2; = 0.245; Adj. R\u0026sup2; = \u0026ndash;0.083; DW = 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 374px;\"\u003e\n \u003cp\u003eLog Likelihood = 163.96; AIC = \u0026ndash;12.747; SC = \u0026ndash;12.604; Obs = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6 The GARCH results for USDT, another stablecoin, confirm its stability and resilience but also reveal distinct dynamics across the crisis and post-COVID phases. During the COVID period (2020\u0026ndash;2021), several variables were statistically significant: USDT returns were negatively correlated with gold, Treasuries, and the USD index, while showing a positive association with equities (S\u0026amp;P 500) and the VIX. This suggests that, although designed to be stable, USDT exhibited mild sensitivity to broader market movements, particularly during periods of financial stress when demand for stable liquidity instruments increased. The variance equation highlights a highly significant ARCH effect, indicating short-run volatility clustering in USDT despite its peg mechanism. In the post-COVID period (2022\u0026ndash;2023), most explanatory variables became insignificant, consistent with greater stabilization of the peg. The only significant driver was Treasuries, with a weak positive coefficient, reflecting minor alignment with government bond stability. The variance equation reveals a negative and significant ARCH effect, indicating that shocks to USDT volatility dissipate quickly, thereby strengthening its credibility as a stable store of value. Overall, these results confirm that while USDT displayed some sensitivity to market stress during the COVID crisis, it performed more consistently in the post-COVID period, supporting its role as a \u003cstrong\u003elow-risk liquidity instrument\u003c/strong\u003e within diversified portfolios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Figure 1. Volatility of Bitcoin (BTC) during COVID-19 and Post-COVID Periods (2020\u0026ndash;2023).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.1 Figure 2. Volatility of Ethereum (ETH) across Crisis and Recovery Phases (2020\u0026ndash;2023).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.2 Figure 3. Volatility of USDC (Stablecoin) 2020\u0026ndash;2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.3 Figure 4. Volatility of USDT (Stablecoin) 2020\u0026ndash;2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Results Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study reveals that digital assets can play a role in diversifying investment portfolios and may serve as safe havens during periods of financial stress. However, the results suggest that Bitcoin and Ethereum experienced high returns during the crisis. Still, they also came with significant volatility and risks, which make them more akin to speculative investments rather than reliable safety nets. Initially, Bitcoin wasn\u0026apos;t strongly linked to traditional safe-haven assets, such as gold or government bonds; however, as it became more closely tied to the broader financial system, its appeal as a haven gradually decreased over time. Ethereum had mixed results: it showed some safe-haven qualities during the crisis, but after the crisis, it performed more like stocks and other riskier assets, suggesting it is moving toward being a speculative asset. This means that while digital assets can offer some protection during times of extreme stress, they are not as reliable as long-term safe havens.\u003c/p\u003e\n\u003cp\u003eStablecoins like USDC and USDT, on the other hand, acted more like low-risk, stable cash equivalents. Their values remained relatively steady, except for some short-term spikes in 2021, particularly with USDT, which was linked to concerns about the reserves backing it. Because they had little connection to risky investments, stablecoins were effective at preserving capital and reducing overall portfolio risk during difficult times. The study highlights a key difference within digital assets: some are high-risk, volatile cryptocurrencies, while others, such as stablecoins, offer stability and predictability similar to those of money market funds. From a practical standpoint, investors should not treat all digital assets uniformly. Bitcoin and Ethereum might help diversify a portfolio during a crisis. Still, stablecoins like USDC and USDT offer more dependable protection against short-term financial shocks and could be considered in strategies aimed at economic stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study offer important insights into the evolution of digital assets in financial markets, particularly their role as safe-haven assets and their capacity to diversify risk during times of crisis, including the aftermath of the COVID-19 pandemic. This finding aligns with earlier research by Baur and Lucey [30] and Dyhrberg [31], which also found that Bitcoin did not strongly act as a haven during the pandemic. This is evident in the weak or negative correlation between Bitcoin and stocks, and the moderate connection with Treasuries. However, this safe-haven role was short-lived; in the aftermath of the pandemic, Bitcoin became more closely tied to traditional markets, which reduced its ability to protect against significant financial risks. This change aligns with the views of Corbet et al. [32] and Goodell and Goutte [33], who argue that digital assets are evolving into more traditional financial products, acting more like investments that people bet on rather than tools for safety, as markets mature. Ethereum followed a similar but more changeable path. Initially, it had strong links to gold and Treasuries, suggesting some safe-haven qualities during crises. However, during recovery, Ethereum became more closely tied to stocks and major financial indicators, such as the US Dollar Index, indicating it had evolved into a risk-taking asset. These results suggest that while cryptocurrencies may act as temporary hedges during periods of global uncertainty, their long-term presence in financial markets reduces their ability to serve as a safe option.\u003c/p\u003e\n\u003cp\u003eStablecoins such as USDC and USDT, on the other hand, showed significantly different behavior. Both remained relatively unchanged throughout the study, indicating that they are trusted as tools to maintain liquidity. Short, quick spikes in 2021, especially with USDT, initially raised concerns about whether the reserves were sufficient and whether there was adequate regulation. Still, these issues soon subsided, and stability was restored. Unlike Bitcoin and Ethereum, USDC and USDT did not exhibit long-term patterns of high volatility, indicating they are low-risk digital assets. These results align with studies by Lyons and Viswanath-Natraj [34] and Foley et al. [35], who found that stablecoins help maintain stability in cryptocurrency systems, particularly during periods of liquidity scarcity. Notably, the study also reveals a split among digital assets: while speculative tokens like Bitcoin and Ethereum offer high returns but behave like risky assets, stablecoins function more like money market alternatives, providing reliability and low volatility. This difference suggests that discussions about digital assets should treat speculative cryptocurrencies and stablecoins separately, rather than treating all digital assets uniformly.\u003c/p\u003e\n\u003cp\u003eThese findings have broader implications for individuals who invest money and for those who create financial plans. For investors, the results indicate that the benefits of diversifying investments into cryptocurrencies depend on the timing. Bitcoin and Ethereum may help protect a portfolio during significant financial crises by offering some protection. Still, in regular market conditions, they behave more like risky investments, which means they aren\u0026apos;t very good at being safe options. Stablecoins, however, maintain their value stability and offer good liquidity, making them a reliable choice for those who want to avoid risk or manage cash in their investment plans. For individuals who create financial policies, the results demonstrate the importance of stablecoins within the economic system.\u003c/p\u003e\n\u003cp\u003eTheir ability to remain stable even during challenging market times demonstrates their value in financial progress. Still, it also raises concerns about the amount of money kept in reserve, the transparency of their operations, and how to manage risks that could impact the Financial Stability Board (FSB) [36] and the IMF [37] emphasize the need for rules that address both the benefits and risks associated with stablecoins. Overall, the discussion presents a complex picture: cryptocurrencies aren\u0026apos;t as dependable as gold or government bonds when it comes to being safe investments, although they may help diversify a portfolio during major crises.\u003c/p\u003e\n\u003cp\u003eStablecoins, on the other hand, have become the most steady and reliable digital tools for protecting against risks and keeping money liquid. This highlights the importance of clearly distinguishing between different types of digital assets in both research and when developing financial policies.\u003c/p\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study examined how digital assets diversified and served as safe havens during two distinct periods: the COVID-19 crisis (2020\u0026ndash;2021) and the post-crisis period (2022\u0026ndash;2023). We employed simple statistics, correlation checks, and GARCH models to examine the behavior of Bitcoin, Ethereum, and two primary stablecoins (USDC and USDT) in comparison to traditional financial assets, including stocks, gold, government bonds, and economic indicators. The results revealed a distinct difference between speculative cryptocurrencies and stablecoins. During the crisis, Bitcoin and Ethereum didn\u0026rsquo;t really act as safe havens. They had weak or negative links with stocks and some connection with gold and bonds, which is similar to what Bouri et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] found. However, in the aftermath of the crisis, these cryptocurrencies became increasingly linked to riskier markets, rendering them less of a safe option and more akin to speculative investments. Stablecoins, on the other hand, consistently demonstrated stability and helped preserve value throughout the entire period.\u003c/p\u003e \u003cp\u003eTheir price didn\u0026rsquo;t fluctuate significantly, except for brief periods in 2021, so they acted more like low-risk money market tools, which aligns with what Aramonte et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and Huynh et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] stated. These differences demonstrate that the digital asset world is uniform. Cryptocurrencies may offer high returns and some assistance in times of significant shocks. Still, stablecoins are more reliable and can support risk management, as well as maintain money flow in mixed investment setups.\u003c/p\u003e \u003cp\u003eThe key point is that policymakers, regulators, and large investors should not view all digital assets uniformly. They should distinguish between speculative tokens, which are becoming increasingly financial and risky, and stablecoins, which are becoming key tools for facilitating money movement in both regular and decentralized finance. This distinction is crucial for creating rules, planning investments, and managing risks as digital finance continues to evolve. Ultimately, the study concludes that while digital assets cannot rival gold or government bonds as safe havens for money, stablecoins have the potential to enhance portfolio stability and support financial resilience across various market conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theory Contribution and Practical Implications:\u003c/h2\u003e \u003cp\u003eThis study makes a significant contribution to the literature on digital assets and financial stability in three key ways. First, it provides one of the earliest long-horizon analyses of safe-haven dynamics, covering both the COVID-19 crisis (2020\u0026ndash;2021) and the post-COVID recovery (2022\u0026ndash;2023). This dual-phase approach surpasses earlier crisis-only studies, offering a more comprehensive perspective on asset behavior across various market regimes. Second, the study distinguishes between volatile cryptocurrencies (Bitcoin and Ethereum) and relatively stable digital assets (USDC and USDT), demonstrating that these categories exhibit fundamentally different volatility profiles, correlations, and safe-haven properties. This differentiation addresses a gap in prior research, which often treated digital assets as a homogeneous class. Third, the study connects empirical evidence to global regulatory debates, including frameworks advanced by the IMF, FSB, and the EU\u0026rsquo;s MiCA regulation. This linkage highlights the systemic implications of digital assets and bridges financial econometrics with policy design.\u003c/p\u003e \u003cp\u003eFrom a practical perspective, the results offer clear guidance for investors, portfolio managers, and policymakers. For investors, Bitcoin and Ethereum may serve as opportunistic diversifiers during periods of extreme stress, but their volatility makes them unreliable as long-term safe havens. Stablecoins\u0026mdash;particularly USDC\u0026mdash;proved to be effective liquidity-preserving instruments, suitable for short-term hedging and portfolio risk management. For policymakers, the evidence underscores the importance of integrating stablecoins into financial stability frameworks with emphasis on transparency, reserve adequacy, and systemic oversight. Ultimately, the study demonstrates that digital assets cannot be uniformly classified as safe havens; instead, they play differentiated roles that present both opportunities and challenges for building resilient financial systems in the post-pandemic era.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Recommendations\u003c/h2\u003e \u003cp\u003eBased on the study's findings, several suggestions are offered for investors, regulators, and policymakers seeking to utilize digital assets in a manner that leverages their benefits while mitigating the associated risks. For investors, the results indicate that distinct strategies are required when incorporating digital assets into investment portfolios.\u003c/p\u003e \u003cp\u003eBitcoin and Ethereum should be viewed as high-risk, speculative investments. They might help diversify a portfolio during times of big market shocks, but they don\u0026rsquo;t reliably act as safe investments during calm periods. Because of this, portfolio managers are advised to keep their exposure to these assets within limits that are commensurate with their risk level. They should also pair these assets with more traditional methods of protecting against losses, such as gold or government bonds [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. On the other hand, stablecoins like USDC and USDT offer a reliable way to keep money safe and can serve as an alternative to short-term cash management tools. Especially for large institutions, these stablecoins can be particularly helpful during financial crises, as they prevent significant drops in portfolio value. For regulators and policymakers, the evidence suggests that it's crucial to establish robust and transparent rules for stablecoins.\u003c/p\u003e \u003cp\u003eAlthough USDC and USDT have been pretty stable over most of the time studied, there were brief periods in 2021 where their volatility spiked. These events highlight weaknesses in how reserves are managed and the level of confidence the market has in them. Groups like the Financial Stability Board and the Bank for International Settlements have already discussed the significant implications of stablecoins [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and our research supports the need for increased supervision, greater transparency regarding reserves, and clear rules to prevent risks from spreading. Regulators should also treat speculative cryptocurrencies and stablecoins differently in their regulations, as they serve distinct roles in the financial system.\u003c/p\u003e \u003cp\u003eFor future policies, a balanced approach is suggested. Instead of imposing strict limits on all digital assets, authorities should support innovation in the stablecoin sector while ensuring the system remains secure. This includes facilitating the integration of stablecoins with central bank digital currencies (CBDCs) and ensuring adherence to anti-money laundering (AML) and know-your-customer (KYC) regulations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This approach would help strengthen digital finance systems and enable them to be safely integrated into regular financial markets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eThis study provides valuable insights into how digital assets diversify and serve as safe havens; however, several factors must be taken into consideration. First, we examined four primary digital assets\u0026mdash;Bitcoin, Ethereum, USDC, and USDT\u0026mdash;over the period from 2020 to 2023. While this covers the crisis and post-COVID trends well, it overlooks other cryptocurrencies and stablecoins that may have different risks and returns. Future research could include more tokens, such as Binance Coin, Ripple, or algorithmic stablecoins, to gain a better understanding of the entire digital asset world [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, the study primarily employed GARCH models to examine volatility. These models work well for financial data, but they might not show all the complex connections or changes in digital asset markets. Using more advanced methods, such as multivariate GARCH, copula models, or machine learning, for predicting volatility can provide more profound insights into how assets interact with each other and how they manage extreme risks [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, the study didn\u0026rsquo;t examine high-frequency data, which could provide more detailed insights into how assets interact during extremely stressful market periods.\u003c/p\u003e \u003cp\u003eThird, the study adopts a global perspective but does not specifically examine regional differences in how people invest, the rules governing these investments, or the extent to which these assets are utilized in various areas. Digital markets are influenced by numerous factors in multiple locations, ranging from supportive laws in Switzerland and Singapore to stringent regulations in China. Future work should compare different countries to examine how regulations and market development impact the safety of digital assets [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLastly, while this study yields essential results, digital finance is evolving rapidly, and new risks and opportunities are emerging that are not currently captured in the existing data.\u003c/p\u003e \u003cp\u003eFuture studies could explore the interaction between digital assets and central bank digital currencies (CBDCs), the functioning of decentralized finance (DeFi), and the risks associated with the widespread use of stablecoins [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These areas would help improve knowledge and guide both policy and investor decision-making in a more digital financial system.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eThis study did not involve human participants or animals; therefore, ethics approval was not required.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author declares no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe sole author confirms that they were responsible for the conception, design, data collection, analysis, interpretation, and writing of the manuscript \"*\" Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eBaur, D.G., \u0026amp; Lucey, B.M. 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Dynamic connectedness and integration in cryptocurrency markets. \u003cem\u003eInternational Review of Financial Analysis, 63\u003c/em\u003e, 257\u0026ndash;272. https://doi.org/10.1016/j.irfa.2018.12.002\u003c/li\u003e\n\u003cli\u003eZeng, T., Yang, M., \u0026amp; He, F. (2020). Daily return volatility forecasting of cryptocurrencies by a mixed data sampling model. \u003cem\u003ePhysica A: Statistical Mechanics and its Applications, 540\u003c/em\u003e, 122868. https://doi.org/10.1016/j.physa.2019.122868\u003c/li\u003e\n\u003cli\u003eSch\u0026auml;r, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets. \u003cem\u003eFederal Reserve Bank of St. Louis Review, 103\u003c/em\u003e(2), 153\u0026ndash;174. https://doi.org/10.20955/r.103.153-74\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digital assets, haven, portfolio diversification, volatility clustering, GARCH model, Bitcoin (BTC), Ethereum (ETH), stablecoins (USDC, USDT), COVID-19 crisis, post-COVID financial markets","lastPublishedDoi":"10.21203/rs.3.rs-7808569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7808569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the evolving safe-haven properties and diversification benefits of digital assets across two distinct phases of global financial uncertainty: the COVID-19 crisis (January 2020\u0026ndash;December 2021) and the post-COVID recovery period (January 2022\u0026ndash;December 2023). Using GARCH (1,1) models and comparative volatility analysis, the research assesses the risk management performance of Bitcoin (BTC), Ethereum (ETH), and two leading stablecoins (USDC and USDT) under systemic stress. The findings reveal that while Bitcoin and Ethereum experienced high volatility persistence and limited hedging effectiveness during the crisis, stablecoins consistently provided low-variance characteristics, with USDC demonstrating remarkable resilience. Post-COVID results confirm a structural transition, as digital assets displayed improved volatility dynamics but continued to pose regulatory and systemic concerns. Unlike earlier studies that were restricted to the crisis period, this paper provides the first extended two-phase analysis, connecting empirical evidence to the global regulatory discourse, including debates on stablecoin oversight, systemic risk buffers, and financial stability frameworks. By integrating asset-level volatility outcomes with policy implications, the study contributes to understanding the dual role of digital assets as both high-risk diversifiers and potential regulatory instruments. The results provide valuable insights for investors, central banks, and policymakers in designing post-pandemic financial resilience strategies.\u003c/p\u003e","manuscriptTitle":"Digital Assets as Safe Havens Portfolio Diversification Benefits Across Crisis and Post-COVID Periods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-14 06:58:31","doi":"10.21203/rs.3.rs-7808569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T04:35:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T11:08:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79073461537291623546585362329219746512","date":"2026-02-08T21:59:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125599727288180418223267285631463436254","date":"2026-02-08T06:20:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T05:37:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T06:28:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T09:42:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-28T04:54:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-10-28T04:50:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ab18e0af-7c42-4ffd-b5eb-ffb8c290ef04","owner":[],"postedDate":"February 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":62697907,"name":"Business and commerce/Finance"},{"id":62697908,"name":"Social science/Finance"},{"id":62697909,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-14T06:58:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-14 06:58:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7808569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7808569","identity":"rs-7808569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}