Volatility Connectedness Between Digital Assets and Sustainable Finance

Volatility Connectedness Between Digital Assets and Sustainable Finance | 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 Research Article Volatility Connectedness Between Digital Assets and Sustainable Finance Rupinder Katoch, Samoon Khan, Caroline Lindah Mphande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8195989/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This paper investigates the volatility spillover and connectedness dynamics at the intersection of the rapidly expanding digital asset ecosystem and the sustainable finance sector, motivated by the growing need to manage both financial and environmental regulatory risks. Employing the Diebold-Yilmaz (2012) spillover index framework based on a rolling-window Vector Autoregression (VAR) model, we analyze daily returns of a diverse portfolio of 30 assets from 2020 to 2025. The portfolio includes DeFi tokens (AAVE, Maker), NFTs and utility tokens (WAX, Chiliz), foundational cryptocurrencies, and thematic ETFs focused on clean energy and carbon (ICLN, CRBN). The empirical results reveal a highly interconnected system with a Total Connectedness Index (TCI) of 76.03%. We identify thematic ETFs (KGRN, ICLN) and key infrastructure tokens like Chainlink (NET: 30.35) as the primary net transmitters of volatility. Conversely, cryptocurrencies such as Stacks (NET: − 75.52) and the stablecoin DAI (NET: -34.02) are the largest net receivers of systemic shocks. A pivotal finding is the pronounced sectoral clustering within asset classes, alongside remarkably low cross-sector spillover (below 2%) between the digital asset and ETF groups. This informational boundary suggests significant diversification benefits. The findings provide a quantitative basis for constructing resilient portfolios, highlighting the strategic role of Carbon and Clean Energy ETFs not only for financial diversification but also as a crucial hedge against the inherent carbon-related regulatory risks in the digital asset market. Volatility Spillover Connectedness Diebold-Yilmaz Cryptocurrencies DeFi Sustainable Finance Carbon ETFs Systemic Risk Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The global financial landscape is currently being reshaped by two powerful, parallel forces: the explosive growth of the digital asset ecosystem and the inexorable rise of sustainable finance. On one hand, the evolution from Bitcoin's inception to a multi-trillion-dollar market encompassing Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) has presented unprecedented opportunities and complex new risks (Corbet, Lucey, et al., 2018 ; Kou, Li, et al., 2021 ). Characterized by extreme volatility, non-normal return distributions, and dynamic correlations, this asset class continues to attract significant attention from both retail and institutional investors seeking high returns and diversification (Bouri, Molnár, et al., 2017 ; Ji, Bouri, et al., 2019 ). The DeFi sector, in particular, aims to rebuild the financial system on decentralized networks, while NFTs have created novel markets for digital ownership, each bringing its own unique risk-return profile (Aharon et al., 2022 ; Angerer et al., 2022 ). Simultaneously, a paradigm shift towards sustainability has embedded Environmental, Social, and Governance (ESG) considerations into the core of modern investment strategy (Schoenmaker & Schramade, 2019 ). In response to the escalating climate crisis, specialized financial instruments such as Clean Energy and Carbon Exchange-Traded Funds (ETFs) have gained prominence. These assets allow investors to gain exposure to the green energy transition and to hedge against climate policy and carbon pricing risks (Batten, Kinateder, & Wagner, 2021 ; Saeed, Bouri, & Vo, 2020 ). The financial dynamics of these green assets, including their volatility and connections to traditional energy and equity markets, have become a focal point of academic inquiry (Balcilar, Demirer, & Hammoudeh, 2013 ; Reboredo, 2018 ). The critical intersection of these two domains forms the central problem of this research. The very technology underpinning many prominent digital assets—Proof-of-Work consensus mechanisms—is notoriously energy-intensive, with a carbon footprint comparable to that of entire nations (de Vries, 2018 ; Gallersdörfer, Klaaßen, & Stoll, 2020 ). This "dark side" of the digital revolution creates a profound conflict for the modern investor and poses a significant, non-financial risk: the prospect of stringent environmental regulation (Truby, 2018 ; Mora et al., 2018 ). As climate policy uncertainty grows, the risk of carbon taxes, trading restrictions, or outright bans on the most carbon-intensive tokens looms large, potentially inducing systemic shocks within the crypto-market (Ren & Lucey, 2022 ; Umar, Riaz, & Ahmed, 2020 ). This precarious situation gives rise to a crucial research question: What is the nature and magnitude of risk transmission—or connectedness—within and between the burgeoning digital asset ecosystem and the established sustainable finance sector? Understanding these spillover dynamics is no longer just an academic exercise; it is a practical necessity for constructing resilient, diversified, and environmentally conscious portfolios. While extensive research has mapped the internal connectedness of cryptocurrency markets (Koutmos, 2018 ; Yi, Xu, & Qin, 2018 ) and the linkages between crypto and traditional assets (Shahzad et al., 2019 ; Klein, Hien, & Walther, 2018 ), the specific relationship between a broad suite of advanced digital assets (including DeFi and NFTs) and carbon-focused ETFs remains critically under-explored (Naeem et al., 2021 ; Pham, Karim, & Rifat, 2022 ). To address this gap, this study employs the dynamic connectedness framework pioneered by Diebold and Yilmaz ( 2009 , 2012 , 2014 ). This methodology, grounded in vector autoregression (VAR) modeling, provides a powerful lens through which to quantify the total, directional, and net volatility spillovers across a complex network of assets. Its ability to identify the primary transmitters and receivers of systemic shocks and to track the evolution of these relationships over time makes it exceptionally well-suited for analyzing these fast-moving, interdependent markets. This approach has proven its utility in diverse financial contexts, from global equity markets (Diebold & Yilmaz, 2009 ) to sovereign debt (Antonakakis & Vergos, 2013 ) and, more recently, to the crypto space itself (Yarovaya et al., 2021 ; Yousaf & Yarovaya, 2022 ). This paper makes a significant contribution by being among the first to construct and analyze a comprehensive spillover network that explicitly integrates a diverse portfolio of DeFi tokens, NFTs, and foundational cryptocurrencies with a targeted selection of Clean Energy and Carbon ETFs. The findings provide a quantitative foundation for strategic asset allocation, offering empirical evidence on whether sustainable assets can serve as effective diversifiers or hedges against the unique financial and regulatory risks inherent in the digital asset market. By mapping these risk transmission channels, this research offers invaluable insights for investors, portfolio managers, and policymakers navigating the complex nexus of digital innovation and sustainable finance. The remainder of this paper is structured as follows: Section 2 provides a review of the relevant literature. Section 3 details the research methodology, including the dataset and the Diebold-Yilmaz framework. Section 4 presents and discusses the empirical results of the connectedness analysis. Finally, Section 5 concludes with a summary of the major findings and their practical implications. 2. Review of Literature The proliferation of digital assets and the increasing integration of financial markets have spurred significant academic interest in understanding the nature of risk transmission and interconnectedness. This literature review synthesizes key research streams that form the foundation of this study. It begins by discussing the foundational concept of volatility spillover, moves to its application within and across cryptocurrency markets (including DeFi and NFTs), explores the linkage between digital and traditional financial assets like ETFs, and concludes by examining the nascent but critical intersection of sustainable finance, carbon markets, and digital assets. The study of financial contagion and risk transmission is central to modern finance. The seminal work of Diebold and Yilmaz ( 2009 , 2012 , 2014 ) revolutionized this field by introducing a formal framework to measure connectedness based on forecast error variance decompositions from vector autoregression (VAR) models. This approach avoids the ordering-dependency issues of previous methods and allows for the quantification of total, directional, and net spillovers, making it a powerful tool for mapping systemic risk. Their methodology has been widely adopted to study interconnectedness in various markets, including equity markets (Diebold & Yilmaz, 2009 ), global financial markets during crises (Diebold & Yilmaz, 2012 ), and sovereign debt markets (Antonakakis & Vergos, 2013 ). Subsequent research has extended this framework. For example, Antonakakis, Breite Lechner, and Scharler (2015) incorporated asymmetric effects, showing that spillovers can differ in response to positive and negative shocks. Baruník, Kočenda, and Vácha ( 2016 ) developed a frequency-domain approach to distinguish between short-term and long-term spillovers, highlighting that connectedness dynamics can vary significantly across different time horizons. These methodological advancements have provided a more granular understanding of how shocks propagate through complex financial systems. With the rise of cryptocurrencies, the Diebold-Yilmaz framework became a natural choice for analyzing this new, highly volatile asset class. Early studies by Yi, S., Z., and G., (2018) and Koutmos ( 2018 ) established the existence of significant volatility spillovers within the crypto market, often identifying Bitcoin and Ethereum as the primary transmitters of shocks. Corbet, Meegan, et al. ( 2018 ) further explored the dynamic nature of these connections, noting that spillovers tend to intensify during periods of market stress, a finding consistent with broader financial markets. As the crypto ecosystem matured, research expanded to include newer classes of digital assets. Studies on Decentralized Finance (DeFi) have shown it to be a highly integrated subsystem. For instance, Kou, Li, et al. ( 2021 ) documented strong bidirectional spillovers between major DeFi tokens, suggesting a tightly coupled internal market structure. Similarly, research by Kakinuma ( 2022 ) and Angerer et al. ( 2022 ) found that governance tokens and key DeFi protocols act as central nodes in the risk network. The connectedness of Non-Fungible Tokens (NFTs) is a more recent area of inquiry. Ante ( 2022 ) and Dowling ( 2022 ) provided initial evidence that the NFT market, while connected to broader crypto trends, also exhibits unique idiosyncratic volatility driven by sentiment and platform-specific events. Studies by Yousaf and Yarovaya ( 2022 ) and Aharon et al. ( 2022 ) have examined the spillovers between NFTs, DeFi, and major cryptocurrencies, confirming that while linkages exist, NFTs often behave differently from fungible tokens. A critical question for investors is whether cryptocurrencies offer genuine diversification benefits. This has led to a large body of research on spillovers between crypto and traditional asset classes. Initial findings were mixed, with some studies like Bouri, Molnár, et al. ( 2017 ) and Corbet, Lucey, et al. ( 2018 ) suggesting that cryptocurrencies were largely insulated from traditional financial markets, making them excellent hedges. However, this relationship appears to be time-varying and has strengthened over time as institutional adoption has grown (Shahzad et al., 2019 ; Klein, Hien, & Walther, 2018 ). More recent studies confirm that the hedging properties of cryptocurrencies have weakened. Ji, Bouri, et al. ( 2019 ) found that cryptocurrencies have become more integrated with global financial markets, acting as both transmitters and receivers of shocks. Research focusing on specific asset classes like Exchange-Traded Funds (ETFs) is particularly relevant. For example, Raza, Shahzad, and Kumar ( 2022 ) and Le, Chevallier, and Nguyen ( 2021 ) analyzed the dynamic connectedness between Bitcoin and various thematic ETFs, finding that spillovers are generally low but can spike during periods of extreme market uncertainty. This suggests that diversification benefits, while diminished, may still exist, particularly between niche crypto-assets and specialized ETFs (Kyriazis, 2020 ; Zeng & Cheng, 2021 ). The work of Bouri, Gupta, and Roubaud ( 2019 ) on spillovers between Bitcoin and commodity markets also provides a useful parallel, highlighting the importance of considering cross-asset linkages. The intersection of digital finance and sustainability is an emerging but vital field of research. A primary concern is the substantial energy consumption and associated carbon footprint of Proof-of-Work cryptocurrencies like Bitcoin (de Vries, 2018 ; Gallersdörfer, Klaaßen, & Stoll, 2020 ). This has drawn attention to the potential for regulatory risk, where governments may impose carbon taxes or restrictions on energy-intensive digital assets (Truby, 2018 ; Mora et al., 2018 ). This environmental risk profile makes the study of carbon- and clean energy-related assets, such as Carbon ETFs, highly relevant. The literature on carbon finance has established that carbon prices are influenced by energy prices, economic activity, and policy decisions (Zhang & Wei, 2010 ; Chevallier, 2011 ). The connectedness between carbon markets and broader financial markets has also been explored. Balcilar, Demirer, and Hammoudeh ( 2013 ) found significant volatility spillovers between carbon, energy, and stock markets. More recently, studies by Saeed, Bouri, and Vo ( 2020 ) and Batten, Kinateder, and Wagner ( 2021 ) have examined the role of green financial assets and clean energy stocks as hedges against climate policy risk. However, research explicitly connecting the spillover dynamics of carbon markets, clean energy ETFs, and the cryptocurrency ecosystem is still in its infancy. Some studies have begun to bridge this gap. For example, Naeem et al. ( 2021 ) investigated the relationship between green finance assets and cryptocurrencies, while Ren and Lucey ( 2022 ) explored the impact of climate policy uncertainty on crypto markets. This paper contributes directly to this nascent literature by empirically mapping the spillover network that includes not only DeFi and NFT tokens but also a range of clean energy and carbon-focused ETFs. This allows for a direct test of the hypothesis that these sustainable assets can provide diversification and a hedge against the unique regulatory and environmental risks inherent in the digital asset space (Umar, Riaz, & Ahmed, 2020 ; Pham, Karim, & Rifat, 2022 ). By quantifying the low cross-sectoral spillovers, this study provides an empirical basis for constructing environmentally balanced and financially resilient portfolios. 3. Research Methodology This study employs a multi-stage quantitative approach to analyze the connectedness and volatility spillover dynamics between a selection of digital assets and thematic ETFs. The methodology is designed to first characterize the statistical properties of the asset returns and then to quantify the magnitude, direction, and evolution of risk transmission using the Diebold and Yilmaz ( 2012 ) connectedness framework. 3.1. Data Selection and Preparation The dataset comprises daily price series for a portfolio of 30 assets, categorized as follows as DeFi and Infrastructure Tokens: AAVE, Synthetix (SNX), Dai (DAI), USD Coin (USDC) Maker (MKR), Chainlink (LINK), Tezos (XTZ), Stacks (STX), Fetch.ai (FET). NFT, Metaverse, and Utility Tokens: Enjin Coin (ENJ), Decentraland (MANA), Chiliz (CHZ), THETA, SUPERf, XYO NETWORK, WAX, Basic Attention Token (BAT), Hedera (HBAR), SwftCoin (SWFTC), NameCoin (NMC). Thematic ETFs: Invesco WilderHill Clean Energy ETF (PBW), ALPS Clean Energy ETF (RNRG), Energy Select Sector SPDR Fund (XLE), iShares Global Clean Energy ETF (ICLN), KraneShares Global Carbon Strategy ETF (CRBN), First Trust Global Wind Energy ETF (FAN), Invesco Solar ETF (TAN), SmartETFs Sustainable Energy II ETF (SMOG), KraneShares MSCI China Clean Technology Index ETF (KGRN), and QLCN. The data covers the period from early 2020 to early 2025, providing a comprehensive view of asset behavior across various market cycles, including periods of high and low volatility. To prepare the data for analysis, daily continuously compounded returns (log-returns) were calculated for each asset. This transformation standardizes the series and ensures stationarity, which is a prerequisite for the time-series models employed. The log-return \(\:{r}_{t}\:\) at time t is computed as: $$\:{r}_{t}\:=100\:\bullet\:ln\left(\genfrac{}{}{0pt}{}{{P}_{t}}{{P}_{t-1}}\right)\:$$ 1 where \(\:{P}_{t}\) is the price of the asset at time t and ln is the natural logarithm. The returns are multiplied by 100 to express them in percentage terms. 3.2. Preliminary Statistical Analysis Before modeling the connectedness, a thorough descriptive analysis was conducted to understand the unconditional properties of each return series. Mean, variance, skewness, and excess kurtosis were calculated to summarize the central tendency, dispersion, and shape of the return distributions. Normality Testing The Jarque-Bera (JB) test was used to formally test the null hypothesis that the returns follow a normal distribution. The test statistic is based on the sample skewness and kurtosis. Stationarity Testing The Elliott, Rothenberg, and Stock (ERS) unit root test was applied to each return series to check for stationarity, a crucial assumption for Vector Autoregression (VAR) modeling. The Ljung-Box Q-statistic was calculated for both the return series (Q(20)) and the squared return series (Q²(20)) with a lag of 20. Significant Q(20) statistics indicate the presence of linear serial correlation in returns, while significant Q²(20) statistics suggest the presence of time-varying volatility (volatility clustering or ARCH effects), justifying the focus on volatility spillovers. 3.3. The Diebold-Yilmaz Connectedness Framework Total Connectedness Index (TCI) The Total Connectedness Index at time t measures the overall spillover intensity in the system: $$\:{\text{T}\text{C}\text{I}}_{t}\text{}=\frac{1}{30}\sum\:_{\text{i}=1}^{30}\sum\:_{\frac{j=1}{j\ne\:1}}^{30}\phi\:ij,t\left(H\right)\text{}\times\:100$$ 2 This represents the average percentage of forecast error variance due to cross-variable shocks (excluding own shocks). It’s calculated by summing the pairwise spillover effects across all variables \(\:(i\ne\:j)\) , using the \(\:\phi\:ij,t\left(H\right)\) values, which are the coefficients for the spillover between variables \(\:\mathcal{i}\mathcal{\:}and\:\mathcal{j}\) at time \(\:\mathcal{t}\) with horizon \(\:\mathcal{H}\) . Directional Connectedness Measures These measures how shock are transmitted or received between variables From Others to Variable ii: $$\:{C}_{\text{i},\text{t}}^{from}=\sum\:_{\frac{j=1}{j\ne\:1}}^{30}\phi\:ij,t\left(H\right)\times\:100$$ 3 Measures how much variable i's forecast error variance is explained by shocks from other variables. To Others from Variable ii : $$\:{C}_{\text{i},\text{t}}^{to}=\sum\:_{\frac{j=1}{j\ne\:1}}^{30}\phi\:ij,t\left(H\right)\times\:100$$ 4 Measures the influence of shocks originating from variable i on other variables. Net Connectedness for Variable ii : $$\:{C}_{\text{i},\text{t}}^{net}=\sum\:_{\frac{j=1}{j\ne\:1}}^{30}\phi\:ij,t\left(H\right)\times\:100$$ 5 Positive values mean variable \(\:\mathcal{i}\) is a net transmitter of shocks; negative values indicate it is a net receiver. 3.4. Dynamic Connectedness Analysis : To capture the time-varying nature of market integration, the static analysis is extended to a dynamic framework using a rolling-window approach (Yousaf & Yarovaya, 2022 ). The VAR model and the entire spillover table are re-estimated over a fixed-size rolling window of 200 days with a forecast horizon of 10 days. This process generates time series for the TCI, directional spillovers, and net spillovers, allowing for an examination of how connectedness evolves in response to market events (Kyriazis et al., 2024 ; Mensi et al., 2023 ). All empirical analyses were conducted using the R programming language and its specialized packages for time-series econometrics and connectedness analysis, such as vars and Connectedness Approach. 4. Result Analysis and interpretation 4.1. Data Analysis Table 2 presents descriptive statistics for the variables DAI, Stacks, AAVE, Synthetix, HBAR, Maker, Chainlink, USDC, BAT, SwftCoin, Fetch AI, ENJ Coin, TEZOS, Decentraland, NameCoin, Chiliz, THETA, SUPERf, XYO NETWORK, WAX, PBW, RNRG, XLE, ICLN, CRBN, FAN, TAN, SMOG, KGRN, and QLCN. As expected for log-returns, the mean values for most assets are close to zero, indicating price changes fluctuate around a stable average. Variance levels are heterogeneous: cryptocurrencies such as XYO NETWORK and PBW exhibit pronounced volatility, while assets like Chainlink and Maker display lower variance and greater price stability. The return distributions are distinctly non-normal. Skewness analysis reveals significant asymmetry: AAVE and XYO NETWORK show marked negative skewness (–22.6534 and − 43.2114), indicating frequent large negative returns, whereas assets such as WAX, PBW, and XLE are strongly positively skewed, suggesting a bias toward large positive returns. Excess kurtosis is substantial across all assets, with extremes observed for AAVE (801.0931), XYO NETWORK (1887.4349), and PBW (338.9304), highlighting the presence of fat tails and extreme outliers typical in digital asset markets. The Jarque-Bera (JB) test statistics are extremely high and statistically significant for all series, further confirming the pronounced deviations from normality due to skewness and leptokurtosis. ERS statistics indicate that most assets have values close to one, suggesting non-stationarity in returns, while a few, such as Maker (–1.919) and TEZOS (–2.186), exhibit negative ERS values, potentially reflecting mean-reverting behavior. Ljung-Box Q(20) statistics signal significant autocorrelation in returns for assets like Maker (69.2632), WAX (33854.5643), and CRBN (146.981), and the Q2(20) values indicate widespread volatility clustering. Collectively, these results underscore the complexity and non-standard statistical properties that characterize digital asset and carbon market returns. Table 1 Descriptive Statistics Variable Mean Variance Skewness Ex_Kurtosis JB ERS Q_20 Q2_20 DAI 0 0 1.209 66.4112 357535.633 1 525.9404 1524.7444 Stacks -0.0011 0.0046 -0.666 19.6899 31530.6269 1 27.9548 16.6177 AAVE -0.0047 0.0159 -22.6534 801.0931 52121200.6 0.9996 20.8373 0.0165 Synthetix 0.0002 0.0044 0.1396 5.0146 2042.0901 1 47.4009 132.9269 HBAR -0.0015 0.0042 -1.548 22.9292 43339.7625 1 34.617 228.7392 Maker -0.0006 0.0034 0.9975 38.0412 117479.502 1 69.2632 79.0091 Chainklink -0.0011 0.0034 0.843 11.3422 10645.057 1 49.2889 115.0016 USDC 0 0 -0.4639 54.7355 242619.102 0.9961 212.985 438.0585 BAT 0.0002 0.0033 0.3606 11.2216 10236.6453 1 60.9274 176.2243 SwftCoin -0.0013 0.0073 -1.7884 15.8314 21326.5419 1 22.8843 128.6598 Fetch AI -0.0015 0.0054 0.9879 17.932 26348.843 1 48.0508 14.609 ENJ Coin 0.0001 0.0048 0.0246 8.8726 6373.4232 0.9997 51.8379 329.0437 TEZOS 0.0004 0.0031 0.7323 13.008 13872.5074 1 62.5633 124.4787 Decentraland -0.0012 0.0046 -1.3603 28.6196 66910.6391 1 23.7657 111.5796 NameCoin 0 0.0046 -0.0567 24.1876 47364.9381 1 150.1615 225.664 Chiliz -0.0009 0.0044 -0.2258 20.8987 35375.5667 1 40.8859 238.1464 THETA -0.0011 0.0039 0.7878 9.6585 7753.2897 1 41.7324 79.9225 SUPERf -0.0002 0.0038 -0.8348 35.3413 101343.052 1 118.818 233.8917 XYO NETWORK -0.0082 0.0371 -43.2114 1887.4349 289011501 1 32.4435 0.1039 WAX 0.108 0.0156 2.8286 9.8916 10512.1036 1 33854.5643 25777.5033 PBW 0.0017 0.0016 12.063 338.9304 9347115.45 1 163.9458 3.3414 RNRG 0.0009 0.0003 0.7902 6.612 3739.7116 1 316.9926 3282.1986 XLE 0.0005 0.0015 13.0823 405.8768 13392171 1 295.7329 15.4515 ICLN 0.0002 0.0005 1.1276 13.0628 14218.8513 1 367.9613 783.8645 CRBN 0.0002 0.0002 6.8986 150.8225 1857004.59 1 146.981 17.6425 FAN 0.0002 0.0003 0.4592 7.5877 4729.3506 0.9999 263.1873 1790.0936 TAN 0.0008 0.0009 1.038 10.0395 8508.9003 1 271.2649 518.3886 SMOG 0.0003 0.0005 2.8616 33.5052 93535.4721 1 358.561 467.0772 KGRN 0 0.0007 0.569 9.5301 7457.7487 1 282.1573 242.6686 QLCN -0.0003 0.0008 0.6977 7.2386 4397.443 1 333.8056 593.9039 4.2. Connectedness Analysis This section presents a comprehensive spillover and connectedness analysis between NFT tokens, DeFi tokens, and Exchange-Traded Funds (ETFs), focusing on their implications for constructing robust, environmentally balanced portfolios. The analysis is based on the Diebold-Yilmaz (2012) spillover index and is visualized through multiple network diagrams, volatility plots, and return time series (see Figs. 1–9). This approach enables a nuanced assessment of risk transmission channels, market integration, and the potential role of Carbon ETFs as both financial and sustainability hedges. The diagonal elements of the spillover matrix (see Fig. 1) reveal considerable variation in own-variance contributions among the studied assets. Stablecoins such as DAI and USDC display the highest self-influence (56.91% and 58.23%, respectively), indicating a strong insulation from external market shocks. NameCoin (33.00%) also exhibits a relatively high own-variance. These characteristics position such tokens as potential stability anchors in a multi-asset portfolio, particularly valuable for investors seeking to mitigate contagion risk in highly volatile digital asset markets. In contrast, table-3 Stacks (14.42%), AAVE (31.78%), and BAT (15.19%) show much lower own-variance contributions, highlighting their elevated exposure to system-wide shocks. DAI’s profile as a net receiver (NET: − 34.02) is consistent with its intended design as a stablecoin, although its residual interconnectedness (TO value: 23.20, notably 4.66 to USDC) suggests even the most insulated tokens are not immune from systemic risk within the crypto-finance ecosystem. Table 2 Static Spillover Matrix Asset TO Others FROM Others NET Spillover Top 5 Net Transmitters KGRN 65.45 122.85 36.06 TEZOS 115.18 14.75 29.93 QLCN 113.27 13.65 26.93 BAT 109.70 15.19 24.89 ICLN 110.83 13.71 24.54 Top 5 Net Receivers Stacks 13.11 85.58 -72.47 WAX 25.51 64.77 -39.26 DAI 13.23 43.09 -29.87 XLE 40.80 68.49 -27.69 NameCoin 42.42 67.00 -24.59 The net spillover table-4 (see Fig. 2) highlights Chainlink as the dominant shock transmitter among blockchain assets (NET: 30.35), with significant outward spillovers (TO: 114.85) impacting BAT, TEZOS, and Decentraland. BAT emerges as another powerful net transmitter (NET: 36.95; TO: 121.79), indicating that certain utility tokens may exert disproportionate influence over broader market dynamics, possibly due to their integration in multiple DeFi and NFT use cases. DeFi blue chips such as AAVE and Synthetix (NET: 14.38 and 19.29, respectively) similarly act as net transmitters, aligning with their pivotal roles in on-chain financial infrastructure. On the other hand, Stacks (NET: − 75.52), XYO NETWORK (NET: − 58.65), and WAX (NET: − 60.26) function primarily as shock absorbers. Their returns largely react to, rather than propagate, systemic movements—an important consideration for portfolio construction when seeking risk-mitigating components. Analysis of the full spillover network (Figs. 3 and 4) reveals pronounced sectoral clustering. DeFi assets such as AAVE, Synthetix, and Maker exhibit strong bidirectional spillovers (typically 3–7%), reflecting deep interconnectedness within the DeFi sub-ecosystem. Clean energy ETFs—TAN, ICLN, FAN—demonstrate even higher intra-sector spillovers (8–12%), likely due to shared exposure to global energy transition themes. By contrast, cross-sector spillovers between blockchain tokens and ETFs generally remain below 2%, confirming significant informational and risk boundaries between these domains (Fig. 5, Volatility Spillovers for Different Assets). This limited cross-sector transmission is particularly significant for sustainable portfolio construction, as it supports the notion that adding Carbon ETFs to a crypto-heavy portfolio can deliver genuine diversification, limiting exposure to correlated drawdowns during market turmoil. Directional analysis (see network graphs, Fig. 3and table-5) confirms a hierarchical market structure. Central nodes—predominantly DeFi blue chips and major altcoins—function as the primary initiators of risk propagation, with smaller tokens and ETFs largely reacting to these systemic movements. The Total Connectedness Index (Fig. 3) quantifies this dynamic, indicating that approximately 80% of the variance across assets is attributable to cross-asset spillovers, while only a minority is due to idiosyncratic shocks. Notably, the Net Spillover plot (Fig. 4) reveals distinct asymmetries between transmitters and absorbers, confirming the strategic need to balance risk origins and destinations in portfolio design. A unique feature of this research is the explicit consideration of carbon risk and environmental impact in portfolio construction. NFTs and DeFi tokens, by virtue of their reliance on energy-intensive blockchain protocols, are associated with substantial carbon emissions. If these emissions persist or intensify, regulatory authorities may be compelled to restrict or ban trading in the most carbon-intensive tokens. This regulatory uncertainty amplifies the need for integrating Carbon ETFs (e.g., CRBN, ICLN, SMOG) within digital asset portfolios—not just as a financial diversification tool, but also as a forward-looking hedge against carbon risk and future regulatory shocks. Analysis indicates that while carbon and clean energy ETFs exhibit modest spillover to and from digital assets (e.g., SUPERf transmits to PBW, QLCN, CRBN; clean energy ETFs like QLCN also transmit minor shocks to AAVE and Maker), their primary value lies in their relative independence and strong intra-sector cohesion. This finding provides direct support for environmentally conscious investors: portfolios that combine blockchain-based tokens and Carbon ETFs can achieve both sustainability objectives and enhanced resilience against idiosyncratic and regulatory risk. The low level of cross-sector spillovers (crypto-to-ETF and vice versa) means meaningful diversification is attainable by combining these asset classes. This is especially pertinent as crypto assets remain highly volatile relative to ETFs (Fig. 4, Returns of All Assets Over Time). High own-variance contributors (DAI, USDC) should be strategically weighted as portfolio stabilizers. Central transmitters (Chainlink, BAT, TEZOS) require careful monitoring and potentially smaller allocations to avoid outsized systemic risk. Carbon ETFs should be incorporated for those wishing to hedge environmental risks, anticipate policy changes, and build climate-aligned portfolios. This connectedness analysis establishes a quantitative foundation for multi-asset, sustainability-oriented portfolio management. By empirically demonstrating sectoral clustering, the centrality of key transmitters, and the low cross-sector spillover between blockchain assets and carbon ETFs, the results directly support the strategic inclusion of Carbon ETFs for balancing both financial and environmental risk. As NFTs and DeFi tokens continue to generate substantial carbon emissions, the risk of regulatory intervention looms ever larger, reinforcing the need for proactive portfolio diversification. These findings set the stage for further price prediction and regime-shift modeling using SETAR and Hurst exponent approaches, which will be discussed in the following sections to provide a holistic risk management framework. The connectedness analysis reveals a highly integrated system of crypto-assets and thematic ETFs, with a Total Connectedness Index of 76.03%. Thematic ETFs, particularly those related to clean and green energy (KGRN, ICLN, QLCN), act as major net transmitters of volatility, highlighting their systemic importance in this specific asset network. On the other hand, several cryptocurrencies, most notably Stacks and the stablecoin DAI, are strong net receivers of shocks. The dynamic analysis underscores that these relationships are not static; the overall system risk and the roles of individual assets fluctuate significantly over time, reacting to changing market conditions. These findings have critical implications for portfolio diversification, risk management, and understanding financial stability in a market that increasingly links traditional thematic investments with digital assets. 5. Conclusion This research provides a comprehensive analysis of the volatility spillover and connectedness dynamics between a diverse portfolio of cryptocurrencies including NFT and DeFi tokens and thematic Exchange-Traded Funds (ETFs), with a specific emphasis on carbon and clean energy assets. By employing the Diebold-Yilmaz (2012) spillover index and a thorough examination of the assets' statistical properties, this paper offers critical insights for multi-asset portfolio construction, risk management, and sustainable investing (Kilic et al., 2022 ; De Sousa Gabriel et al., 2025 ). The analysis reveals a tightly integrated system, evidenced by a Total Connectedness Index (TCI) of 76.03%. This indicates that over three-quarters of the volatility in the examined assets is driven by spillovers from others in the network, while less than a quarter stems from asset-specific (idiosyncratic) shocks. This high level of interconnectedness underscores the paramount importance of understanding systemic risk over individual asset behavior (Nadeem et al., 2025 ; Abdullah et al., 2025 ). The study identifies clear, asymmetric roles among assets. Thematic ETFs, particularly those related to clean energy and technology (KGRN, ICLN, QLCN), along with key blockchain infrastructure tokens like Chainlink (NET: 30.35) and utility tokens like BAT (NET: 36.95), are the primary net transmitters of volatility. They are the main sources of systemic risk propagation within this ecosystem. In stark contrast, cryptocurrencies such as Stacks (NET: − 75.52), WAX (NET: − 60.26), and XYO NETWORK (NET: − 58.65) are overwhelmingly net receivers. Their volatility is largely a reaction to system-wide movements, positioning them as potential diversifiers against the primary risk drivers. Stablecoins DAI (NET-34.02) and USDC exhibit the highest own-variance contributions (56.91% and 58.23%, respectively), confirming their design as anchors of stability. However, their notable spillover connections, particularly to each other, show they are not entirely immune to systemic contagion. A pivotal finding is the strong intra-sector connectedness. DeFi tokens (e.g., AAVE, Synthetix) exhibit powerful bidirectional spillovers among themselves, as do the clean energy ETFs (e.g., TAN, ICLN, FAN), which show even higher internal cohesion (8–12%). Critically, the cross-sector spillovers between the crypto-asset cluster and the ETF cluster are remarkably low, generally remaining below 2%. This creates a distinct risk boundary and presents a clear, quantifiable opportunity for effective diversification. The descriptive statistics confirm that the returns of all assets profoundly deviate from normality. Extreme negative skewness in assets like AAVE (–22.65) and XYO NETWORK (–43.21), alongside extreme positive skewness in WAX and PBW, points to frequent, large, and asymmetric price movements. Massive excess kurtosis, especially for XYO NETWORK (1887.43) and AAVE (801.09), highlights the prevalence of "fat tails" and extreme event risk, rendering traditional risk models based on normal distributions inadequate for this asset space. 5.1. Implications for Portfolio Management and Sustainable Investing The low cross-sector spillover between cryptocurrencies and thematic ETFs provides a strong empirical foundation for portfolio diversification. Combining these two asset classes can significantly mitigate risk, as shocks originating in one sector do not readily transmit to the other. The reliance of many digital assets on energy-intensive protocols creates a significant, non-financial risk: the potential for regulatory intervention driven by environmental concerns. Including Carbon and Clean Energy ETFs (CRBN, ICLN, SMOG) in a digital asset portfolio serves a dual purpose. Financially, they provide diversification. Strategically, they act as a hedge against future carbon-related regulatory shocks that could negatively impact the crypto market. A sophisticated portfolio strategy should account for each asset's systemic role. The high net spillovers from transmitters like Chainlink, BAT, and key ETFs demand careful risk budgeting and potentially smaller allocations. Conversely, net receivers like Stacks and WAX can be used to balance a portfolio's overall risk profile. The stability of DAI and USDC makes them suitable as core, risk-mitigating holdings. This paper systematically maps the architecture of risk transmission across digital and sustainable asset classes. It moves beyond conventional financial analysis to integrate environmental risk as a core portfolio construction variable. By demonstrating the distinct clustering of risk and the low degree of spillover between crypto-assets and carbon-focused ETFs, the research provides a clear, data-driven rationale for building integrated portfolios that are not only financially resilient but also strategically positioned for a future where environmental sustainability and regulatory landscapes are increasingly intertwined. Declarations Author Contribution The authors made contribution to this paper as follows: R.P wrote the methodology and analysis chapters, S.K wrote the introduction and literature review chapters and C.L.M wrote the analysis and conclusion chapters. Acknowledgments: We are grateful to the anonymous reviewers for their valuable suggestions and comments on our manuscript. Data Availability Statement: The data that support the findings of this study are available from the corresponding author, upon reasonable request. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. Declaration of Conflicting Interests: The authors declared no potential conflicts Ethics, Consent to Participate, and Consent to Publish declarations: Not applicable. Clinical trial number: Not applicable. References Abdullah M, Chowdhury MaF, Ullah GW. 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Zhang YJ, Wei YM. The crude oil market and the gold market: Evidence for cointegration, causality and price discovery. Resour Policy. 2010;35(3):168–77. https://doi.org/10.1016/j.resourpol.2010.05.003 . Additional Declarations No competing interests reported. Supplementary Files table.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Dec, 2025 Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 14 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 30 Nov, 2025 Editor invited by journal 30 Nov, 2025 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 24 Nov, 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. 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2","display":"","copyAsset":false,"role":"figure","size":187262,"visible":true,"origin":"","legend":"\u003cp\u003eSpillover plots (TCI, Net Spillover, From Spillover, To Spillover)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8195989/v1/62551ee413b1860a9c99cf20.jpeg"},{"id":96879595,"identity":"eb1ee151-3bc5-4afe-a840-ed88688736cb","added_by":"auto","created_at":"2025-11-27 06:39:38","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":642706,"visible":true,"origin":"","legend":"\u003cp\u003eSpillover transmitter and receiver and Volatility spillover for different assets\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8195989/v1/4c61e16667119cec94af9f79.jpeg"},{"id":96920246,"identity":"33434fce-09b6-4ca5-a989-08581816f3b4","added_by":"auto","created_at":"2025-11-27 14:14:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":678999,"visible":true,"origin":"","legend":"\u003cp\u003eAll assets connectedness and each assests connectedness frequency\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8195989/v1/18548c53b16715422f66933a.jpeg"},{"id":97136135,"identity":"4689d32c-6470-4f0a-b365-6da8dd896361","added_by":"auto","created_at":"2025-12-01 09:55:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2580189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8195989/v1/f0350d4d-d3b8-4a76-8c77-c0bec993cdb8.pdf"},{"id":96879593,"identity":"798e70cf-df4e-4246-9c11-9d8012a509a3","added_by":"auto","created_at":"2025-11-27 06:39:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68123,"visible":true,"origin":"","legend":"","description":"","filename":"table.docx","url":"https://assets-eu.researchsquare.com/files/rs-8195989/v1/58d3458eed3f745a58f26f4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Volatility Connectedness Between Digital Assets and Sustainable Finance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global financial landscape is currently being reshaped by two powerful, parallel forces: the explosive growth of the digital asset ecosystem and the inexorable rise of sustainable finance. On one hand, the evolution from Bitcoin's inception to a multi-trillion-dollar market encompassing Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) has presented unprecedented opportunities and complex new risks (Corbet, Lucey, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kou, Li, et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Characterized by extreme volatility, non-normal return distributions, and dynamic correlations, this asset class continues to attract significant attention from both retail and institutional investors seeking high returns and diversification (Bouri, Moln\u0026aacute;r, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ji, Bouri, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The DeFi sector, in particular, aims to rebuild the financial system on decentralized networks, while NFTs have created novel markets for digital ownership, each bringing its own unique risk-return profile (Aharon et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Angerer et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimultaneously, a paradigm shift towards sustainability has embedded Environmental, Social, and Governance (ESG) considerations into the core of modern investment strategy (Schoenmaker \u0026amp; Schramade, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In response to the escalating climate crisis, specialized financial instruments such as Clean Energy and Carbon Exchange-Traded Funds (ETFs) have gained prominence. These assets allow investors to gain exposure to the green energy transition and to hedge against climate policy and carbon pricing risks (Batten, Kinateder, \u0026amp; Wagner, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saeed, Bouri, \u0026amp; Vo, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The financial dynamics of these green assets, including their volatility and connections to traditional energy and equity markets, have become a focal point of academic inquiry (Balcilar, Demirer, \u0026amp; Hammoudeh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Reboredo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe critical intersection of these two domains forms the central problem of this research. The very technology underpinning many prominent digital assets\u0026mdash;Proof-of-Work consensus mechanisms\u0026mdash;is notoriously energy-intensive, with a carbon footprint comparable to that of entire nations (de Vries, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gallersd\u0026ouml;rfer, Klaa\u0026szlig;en, \u0026amp; Stoll, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This \"dark side\" of the digital revolution creates a profound conflict for the modern investor and poses a significant, non-financial risk: the prospect of stringent environmental regulation (Truby, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mora et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As climate policy uncertainty grows, the risk of carbon taxes, trading restrictions, or outright bans on the most carbon-intensive tokens looms large, potentially inducing systemic shocks within the crypto-market (Ren \u0026amp; Lucey, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Umar, Riaz, \u0026amp; Ahmed, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis precarious situation gives rise to a crucial research question: What is the nature and magnitude of risk transmission\u0026mdash;or connectedness\u0026mdash;within and between the burgeoning digital asset ecosystem and the established sustainable finance sector? Understanding these spillover dynamics is no longer just an academic exercise; it is a practical necessity for constructing resilient, diversified, and environmentally conscious portfolios. While extensive research has mapped the internal connectedness of cryptocurrency markets (Koutmos, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yi, Xu, \u0026amp; Qin, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the linkages between crypto and traditional assets (Shahzad et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Klein, Hien, \u0026amp; Walther, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the specific relationship between a broad suite of advanced digital assets (including DeFi and NFTs) and carbon-focused ETFs remains critically under-explored (Naeem et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pham, Karim, \u0026amp; Rifat, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address this gap, this study employs the dynamic connectedness framework pioneered by Diebold and Yilmaz (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This methodology, grounded in vector autoregression (VAR) modeling, provides a powerful lens through which to quantify the total, directional, and net volatility spillovers across a complex network of assets. Its ability to identify the primary transmitters and receivers of systemic shocks and to track the evolution of these relationships over time makes it exceptionally well-suited for analyzing these fast-moving, interdependent markets. This approach has proven its utility in diverse financial contexts, from global equity markets (Diebold \u0026amp; Yilmaz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to sovereign debt (Antonakakis \u0026amp; Vergos, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and, more recently, to the crypto space itself (Yarovaya et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yousaf \u0026amp; Yarovaya, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper makes a significant contribution by being among the first to construct and analyze a comprehensive spillover network that explicitly integrates a diverse portfolio of DeFi tokens, NFTs, and foundational cryptocurrencies with a targeted selection of Clean Energy and Carbon ETFs. The findings provide a quantitative foundation for strategic asset allocation, offering empirical evidence on whether sustainable assets can serve as effective diversifiers or hedges against the unique financial and regulatory risks inherent in the digital asset market. By mapping these risk transmission channels, this research offers invaluable insights for investors, portfolio managers, and policymakers navigating the complex nexus of digital innovation and sustainable finance.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a review of the relevant literature. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the research methodology, including the dataset and the Diebold-Yilmaz framework. Section 4 presents and discusses the empirical results of the connectedness analysis. Finally, Section 5 concludes with a summary of the major findings and their practical implications.\u003c/p\u003e"},{"header":"2. Review of Literature","content":"\u003cp\u003eThe proliferation of digital assets and the increasing integration of financial markets have spurred significant academic interest in understanding the nature of risk transmission and interconnectedness. This literature review synthesizes key research streams that form the foundation of this study. It begins by discussing the foundational concept of volatility spillover, moves to its application within and across cryptocurrency markets (including DeFi and NFTs), explores the linkage between digital and traditional financial assets like ETFs, and concludes by examining the nascent but critical intersection of sustainable finance, carbon markets, and digital assets.\u003c/p\u003e\u003cp\u003eThe study of financial contagion and risk transmission is central to modern finance. The seminal work of Diebold and Yilmaz (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) revolutionized this field by introducing a formal framework to measure connectedness based on forecast error variance decompositions from vector autoregression (VAR) models. This approach avoids the ordering-dependency issues of previous methods and allows for the quantification of total, directional, and net spillovers, making it a powerful tool for mapping systemic risk. Their methodology has been widely adopted to study interconnectedness in various markets, including equity markets (Diebold \u0026amp; Yilmaz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), global financial markets during crises (Diebold \u0026amp; Yilmaz, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and sovereign debt markets (Antonakakis \u0026amp; Vergos, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSubsequent research has extended this framework. For example, Antonakakis, Breite Lechner, and Scharler (2015) incorporated asymmetric effects, showing that spillovers can differ in response to positive and negative shocks. Barun\u0026iacute;k, Kočenda, and V\u0026aacute;cha (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) developed a frequency-domain approach to distinguish between short-term and long-term spillovers, highlighting that connectedness dynamics can vary significantly across different time horizons. These methodological advancements have provided a more granular understanding of how shocks propagate through complex financial systems.\u003c/p\u003e\u003cp\u003eWith the rise of cryptocurrencies, the Diebold-Yilmaz framework became a natural choice for analyzing this new, highly volatile asset class. Early studies by Yi, S., Z., and G., (2018) and Koutmos (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) established the existence of significant volatility spillovers within the crypto market, often identifying Bitcoin and Ethereum as the primary transmitters of shocks. Corbet, Meegan, et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) further explored the dynamic nature of these connections, noting that spillovers tend to intensify during periods of market stress, a finding consistent with broader financial markets.\u003c/p\u003e\u003cp\u003eAs the crypto ecosystem matured, research expanded to include newer classes of digital assets. Studies on Decentralized Finance (DeFi) have shown it to be a highly integrated subsystem. For instance, Kou, Li, et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) documented strong bidirectional spillovers between major DeFi tokens, suggesting a tightly coupled internal market structure. Similarly, research by Kakinuma (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Angerer et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that governance tokens and key DeFi protocols act as central nodes in the risk network. The connectedness of Non-Fungible Tokens (NFTs) is a more recent area of inquiry. Ante (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Dowling (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provided initial evidence that the NFT market, while connected to broader crypto trends, also exhibits unique idiosyncratic volatility driven by sentiment and platform-specific events. Studies by Yousaf and Yarovaya (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Aharon et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have examined the spillovers between NFTs, DeFi, and major cryptocurrencies, confirming that while linkages exist, NFTs often behave differently from fungible tokens.\u003c/p\u003e\u003cp\u003eA critical question for investors is whether cryptocurrencies offer genuine diversification benefits. This has led to a large body of research on spillovers between crypto and traditional asset classes. Initial findings were mixed, with some studies like Bouri, Moln\u0026aacute;r, et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Corbet, Lucey, et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggesting that cryptocurrencies were largely insulated from traditional financial markets, making them excellent hedges. However, this relationship appears to be time-varying and has strengthened over time as institutional adoption has grown (Shahzad et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Klein, Hien, \u0026amp; Walther, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMore recent studies confirm that the hedging properties of cryptocurrencies have weakened. Ji, Bouri, et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that cryptocurrencies have become more integrated with global financial markets, acting as both transmitters and receivers of shocks. Research focusing on specific asset classes like Exchange-Traded Funds (ETFs) is particularly relevant. For example, Raza, Shahzad, and Kumar (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Le, Chevallier, and Nguyen (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed the dynamic connectedness between Bitcoin and various thematic ETFs, finding that spillovers are generally low but can spike during periods of extreme market uncertainty. This suggests that diversification benefits, while diminished, may still exist, particularly between niche crypto-assets and specialized ETFs (Kyriazis, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zeng \u0026amp; Cheng, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The work of Bouri, Gupta, and Roubaud (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on spillovers between Bitcoin and commodity markets also provides a useful parallel, highlighting the importance of considering cross-asset linkages.\u003c/p\u003e\u003cp\u003eThe intersection of digital finance and sustainability is an emerging but vital field of research. A primary concern is the substantial energy consumption and associated carbon footprint of Proof-of-Work cryptocurrencies like Bitcoin (de Vries, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gallersd\u0026ouml;rfer, Klaa\u0026szlig;en, \u0026amp; Stoll, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This has drawn attention to the potential for regulatory risk, where governments may impose carbon taxes or restrictions on energy-intensive digital assets (Truby, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mora et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis environmental risk profile makes the study of carbon- and clean energy-related assets, such as Carbon ETFs, highly relevant. The literature on carbon finance has established that carbon prices are influenced by energy prices, economic activity, and policy decisions (Zhang \u0026amp; Wei, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chevallier, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The connectedness between carbon markets and broader financial markets has also been explored. Balcilar, Demirer, and Hammoudeh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found significant volatility spillovers between carbon, energy, and stock markets. More recently, studies by Saeed, Bouri, and Vo (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Batten, Kinateder, and Wagner (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have examined the role of green financial assets and clean energy stocks as hedges against climate policy risk.\u003c/p\u003e\u003cp\u003eHowever, research explicitly connecting the spillover dynamics of carbon markets, clean energy ETFs, and the cryptocurrency ecosystem is still in its infancy. Some studies have begun to bridge this gap. For example, Naeem et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated the relationship between green finance assets and cryptocurrencies, while Ren and Lucey (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explored the impact of climate policy uncertainty on crypto markets. This paper contributes directly to this nascent literature by empirically mapping the spillover network that includes not only DeFi and NFT tokens but also a range of clean energy and carbon-focused ETFs. This allows for a direct test of the hypothesis that these sustainable assets can provide diversification and a hedge against the unique regulatory and environmental risks inherent in the digital asset space (Umar, Riaz, \u0026amp; Ahmed, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pham, Karim, \u0026amp; Rifat, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By quantifying the low cross-sectoral spillovers, this study provides an empirical basis for constructing environmentally balanced and financially resilient portfolios.\u003c/p\u003e"},{"header":"3. Research Methodology","content":"\u003cp\u003eThis study employs a multi-stage quantitative approach to analyze the connectedness and volatility spillover dynamics between a selection of digital assets and thematic ETFs. The methodology is designed to first characterize the statistical properties of the asset returns and then to quantify the magnitude, direction, and evolution of risk transmission using the Diebold and Yilmaz (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) connectedness framework.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Data Selection and Preparation\u003c/h2\u003e\u003cp\u003eThe dataset comprises daily price series for a portfolio of 30 assets, categorized as follows as DeFi and Infrastructure Tokens: AAVE, Synthetix (SNX), Dai (DAI), USD Coin (USDC) Maker (MKR), Chainlink (LINK), Tezos (XTZ), Stacks (STX), Fetch.ai (FET). NFT, Metaverse, and Utility Tokens: Enjin Coin (ENJ), Decentraland (MANA), Chiliz (CHZ), THETA, SUPERf, XYO NETWORK, WAX, Basic Attention Token (BAT), Hedera (HBAR), SwftCoin (SWFTC), NameCoin (NMC). Thematic ETFs: Invesco WilderHill Clean Energy ETF (PBW), ALPS Clean Energy ETF (RNRG), Energy Select Sector SPDR Fund (XLE), iShares Global Clean Energy ETF (ICLN), KraneShares Global Carbon Strategy ETF (CRBN), First Trust Global Wind Energy ETF (FAN), Invesco Solar ETF (TAN), SmartETFs Sustainable Energy II ETF (SMOG), KraneShares MSCI China Clean Technology Index ETF (KGRN), and QLCN.\u003c/p\u003e\u003cp\u003eThe data covers the period from early 2020 to early 2025, providing a comprehensive view of asset behavior across various market cycles, including periods of high and low volatility.\u003c/p\u003e\u003cp\u003eTo prepare the data for analysis, daily continuously compounded returns (log-returns) were calculated for each asset. This transformation standardizes the series and ensures stationarity, which is a prerequisite for the time-series models employed. The log-return \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003eat time \u003cem\u003et\u003c/em\u003e is computed as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{r}_{t}\\:=100\\:\\bullet\\:ln\\left(\\genfrac{}{}{0pt}{}{{P}_{t}}{{P}_{t-1}}\\right)\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the price of the asset at time t and ln is the natural logarithm. The returns are multiplied by 100 to express them in percentage terms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Preliminary Statistical Analysis\u003c/h2\u003e\u003cp\u003eBefore modeling the connectedness, a thorough descriptive analysis was conducted to understand the unconditional properties of each return series. Mean, variance, skewness, and excess kurtosis were calculated to summarize the central tendency, dispersion, and shape of the return distributions. Normality Testing The Jarque-Bera (JB) test was used to formally test the null hypothesis that the returns follow a normal distribution. The test statistic is based on the sample skewness and kurtosis. Stationarity Testing The Elliott, Rothenberg, and Stock (ERS) unit root test was applied to each return series to check for stationarity, a crucial assumption for Vector Autoregression (VAR) modeling. The Ljung-Box Q-statistic was calculated for both the return series (Q(20)) and the squared return series (Q\u0026sup2;(20)) with a lag of 20. Significant Q(20) statistics indicate the presence of linear serial correlation in returns, while significant Q\u0026sup2;(20) statistics suggest the presence of time-varying volatility (volatility clustering or ARCH effects), justifying the focus on volatility spillovers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. The Diebold-Yilmaz Connectedness Framework\u003c/h2\u003e\u003cp\u003e\u003cb\u003eTotal Connectedness Index (TCI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Total Connectedness Index at time t measures the overall spillover intensity in the system:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\text{T}\\text{C}\\text{I}}_{t}\\text{}=\\frac{1}{30}\\sum\\:_{\\text{i}=1}^{30}\\sum\\:_{\\frac{j=1}{j\\ne\\:1}}^{30}\\phi\\:ij,t\\left(H\\right)\\text{}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis represents the average percentage of forecast error variance due to cross-variable shocks (excluding own shocks). It\u0026rsquo;s calculated by summing the pairwise spillover effects across all variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(i\\ne\\:j)\\)\u003c/span\u003e\u003c/span\u003e, using the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\phi\\:ij,t\\left(H\\right)\\)\u003c/span\u003e\u003c/span\u003e values, which are the coefficients for the spillover between variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{i}\\mathcal{\\:}and\\:\\mathcal{j}\\)\u003c/span\u003e\u003c/span\u003e at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{t}\\)\u003c/span\u003e\u003c/span\u003e with horizon \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{H}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDirectional Connectedness Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese measures how shock are transmitted or received between variables\u003c/p\u003e\u003cp\u003eFrom Others to Variable ii:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{C}_{\\text{i},\\text{t}}^{from}=\\sum\\:_{\\frac{j=1}{j\\ne\\:1}}^{30}\\phi\\:ij,t\\left(H\\right)\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeasures how much variable i's forecast error variance is explained by shocks from other variables.\u003c/p\u003e\u003cp\u003eTo Others from Variable \u003cb\u003eii\u003c/b\u003e:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{C}_{\\text{i},\\text{t}}^{to}=\\sum\\:_{\\frac{j=1}{j\\ne\\:1}}^{30}\\phi\\:ij,t\\left(H\\right)\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeasures the influence of shocks originating from variable i on other variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNet Connectedness for Variable ii\u003c/b\u003e:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{C}_{\\text{i},\\text{t}}^{net}=\\sum\\:_{\\frac{j=1}{j\\ne\\:1}}^{30}\\phi\\:ij,t\\left(H\\right)\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePositive values mean variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{i}\\)\u003c/span\u003e\u003c/span\u003e is a net transmitter of shocks; negative values indicate it is a net receiver.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4. Dynamic Connectedness Analysis\u003c/b\u003e: To capture the time-varying nature of market integration, the static analysis is extended to a dynamic framework using a rolling-window approach (Yousaf \u0026amp; Yarovaya, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The VAR model and the entire spillover table are re-estimated over a fixed-size rolling window of 200 days with a forecast horizon of 10 days. This process generates time series for the TCI, directional spillovers, and net spillovers, allowing for an examination of how connectedness evolves in response to market events (Kyriazis et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mensi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll empirical analyses were conducted using the R programming language and its specialized packages for time-series econometrics and connectedness analysis, such as vars and Connectedness Approach.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Result Analysis and interpretation","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Data Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for the variables DAI, Stacks, AAVE, Synthetix, HBAR, Maker, Chainlink, USDC, BAT, SwftCoin, Fetch AI, ENJ Coin, TEZOS, Decentraland, NameCoin, Chiliz, THETA, SUPERf, XYO NETWORK, WAX, PBW, RNRG, XLE, ICLN, CRBN, FAN, TAN, SMOG, KGRN, and QLCN. As expected for log-returns, the mean values for most assets are close to zero, indicating price changes fluctuate around a stable average. Variance levels are heterogeneous: cryptocurrencies such as XYO NETWORK and PBW exhibit pronounced volatility, while assets like Chainlink and Maker display lower variance and greater price stability. The return distributions are distinctly non-normal. Skewness analysis reveals significant asymmetry: AAVE and XYO NETWORK show marked negative skewness (\u0026ndash;22.6534 and \u0026minus;\u0026thinsp;43.2114), indicating frequent large negative returns, whereas assets such as WAX, PBW, and XLE are strongly positively skewed, suggesting a bias toward large positive returns. Excess kurtosis is substantial across all assets, with extremes observed for AAVE (801.0931), XYO NETWORK (1887.4349), and PBW (338.9304), highlighting the presence of fat tails and extreme outliers typical in digital asset markets. The Jarque-Bera (JB) test statistics are extremely high and statistically significant for all series, further confirming the pronounced deviations from normality due to skewness and leptokurtosis. ERS statistics indicate that most assets have values close to one, suggesting non-stationarity in returns, while a few, such as Maker (\u0026ndash;1.919) and TEZOS (\u0026ndash;2.186), exhibit negative ERS values, potentially reflecting mean-reverting behavior. Ljung-Box Q(20) statistics signal significant autocorrelation in returns for assets like Maker (69.2632), WAX (33854.5643), and CRBN (146.981), and the Q2(20) values indicate widespread volatility clustering. Collectively, these results underscore the complexity and non-standard statistical properties that characterize digital asset and carbon market returns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEx_Kurtosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eERS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eQ_20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eQ2_20\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66.4112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e357535.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e525.9404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1524.7444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStacks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.6899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31530.6269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e27.9548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e16.6177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAVE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-22.6534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e801.0931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52121200.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.8373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSynthetix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.0146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2042.0901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e47.4009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e132.9269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.9292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43339.7625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e34.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e228.7392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38.0412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117479.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e69.2632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c9\"\u003e\u003cp\u003e115.0016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUSDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.4639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.7355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e242619.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e212.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e438.0585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.2216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10236.6453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.9274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e176.2243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSwftCoin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.7884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.8314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21326.5419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.8843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e128.6598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFetch AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26348.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e48.0508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14.609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENJ Coin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.8726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6373.4232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e51.8379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e329.0437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTEZOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13872.5074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e62.5633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e124.4787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecentraland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.3603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.6196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66910.6391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e23.7657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e111.5796\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNameCoin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.1876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47364.9381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e150.1615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e225.664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChiliz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.2258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.8987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35375.5667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e40.8859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e238.1464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTHETA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.6585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7753.2897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e41.7324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e79.9225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUPERf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.8348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.3413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e101343.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e118.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e233.8917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXYO NETWORK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-43.2114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1887.4349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e289011501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e32.4435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.8916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10512.1036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e33854.5643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25777.5033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePBW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e338.9304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9347115.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e163.9458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.3414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRNRG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3739.7116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e316.9926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3282.1986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXLE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.0823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e405.8768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13392171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e295.7329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e15.4515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.0628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14218.8513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e367.9613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e783.8645\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRBN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.8986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e150.8225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1857004.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e146.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17.6425\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.5877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4729.3506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e263.1873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1790.0936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.0395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8508.9003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e271.2649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e518.3886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.5052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93535.4721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e358.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e467.0772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKGRN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.5301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7457.7487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e282.1573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e242.6686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQLCN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.2386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4397.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e333.8056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e593.9039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Connectedness Analysis\u003c/h2\u003e\u003cp\u003eThis section presents a comprehensive spillover and connectedness analysis between NFT tokens, DeFi tokens, and Exchange-Traded Funds (ETFs), focusing on their implications for constructing robust, environmentally balanced portfolios. The analysis is based on the Diebold-Yilmaz (2012) spillover index and is visualized through multiple network diagrams, volatility plots, and return time series (see Figs.\u0026nbsp;1\u0026ndash;9). This approach enables a nuanced assessment of risk transmission channels, market integration, and the potential role of Carbon ETFs as both financial and sustainability hedges.\u003c/p\u003e\u003cp\u003eThe diagonal elements of the spillover matrix (see Fig.\u0026nbsp;1) reveal considerable variation in own-variance contributions among the studied assets. Stablecoins such as DAI and USDC display the highest self-influence (56.91% and 58.23%, respectively), indicating a strong insulation from external market shocks. NameCoin (33.00%) also exhibits a relatively high own-variance. These characteristics position such tokens as potential \u003cem\u003estability anchors\u003c/em\u003e in a multi-asset portfolio, particularly valuable for investors seeking to mitigate contagion risk in highly volatile digital asset markets.\u003c/p\u003e\u003cp\u003eIn contrast, table-3 Stacks (14.42%), AAVE (31.78%), and BAT (15.19%) show much lower own-variance contributions, highlighting their elevated exposure to system-wide shocks. DAI\u0026rsquo;s profile as a net receiver (NET: \u0026minus;\u0026thinsp;34.02) is consistent with its intended design as a stablecoin, although its residual interconnectedness (TO value: 23.20, notably 4.66 to USDC) suggests even the most insulated tokens are not immune from systemic risk within the crypto-finance ecosystem.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatic Spillover Matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsset\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO Others\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFROM Others\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNET Spillover\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTop 5 Net Transmitters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKGRN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e36.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTEZOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e115.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e29.93\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQLCN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e26.93\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e24.89\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e24.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTop 5 Net Receivers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStacks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-72.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-39.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-29.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXLE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-27.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNameCoin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-24.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe net spillover table-4 (see Fig.\u0026nbsp;2) highlights Chainlink as the dominant shock transmitter among blockchain assets (NET: 30.35), with significant outward spillovers (TO: 114.85) impacting BAT, TEZOS, and Decentraland. BAT emerges as another powerful net transmitter (NET: 36.95; TO: 121.79), indicating that certain utility tokens may exert disproportionate influence over broader market dynamics, possibly due to their integration in multiple DeFi and NFT use cases. DeFi blue chips such as AAVE and Synthetix (NET: 14.38 and 19.29, respectively) similarly act as net transmitters, aligning with their pivotal roles in on-chain financial infrastructure. On the other hand, Stacks (NET: \u0026minus;\u0026thinsp;75.52), XYO NETWORK (NET: \u0026minus;\u0026thinsp;58.65), and WAX (NET: \u0026minus;\u0026thinsp;60.26) function primarily as shock absorbers. Their returns largely react to, rather than propagate, systemic movements\u0026mdash;an important consideration for portfolio construction when seeking risk-mitigating components.\u003c/p\u003e\u003cp\u003eAnalysis of the full spillover network (Figs.\u0026nbsp;3 and 4) reveals pronounced sectoral clustering. DeFi assets such as AAVE, Synthetix, and Maker exhibit strong bidirectional spillovers (typically 3\u0026ndash;7%), reflecting deep interconnectedness within the DeFi sub-ecosystem. Clean energy ETFs\u0026mdash;TAN, ICLN, FAN\u0026mdash;demonstrate even higher intra-sector spillovers (8\u0026ndash;12%), likely due to shared exposure to global energy transition themes. By contrast, cross-sector spillovers between blockchain tokens and ETFs generally remain below 2%, confirming significant informational and risk boundaries between these domains (Fig.\u0026nbsp;5, Volatility Spillovers for Different Assets). This limited cross-sector transmission is particularly significant for sustainable portfolio construction, as it supports the notion that adding Carbon ETFs to a crypto-heavy portfolio can deliver genuine diversification, limiting exposure to correlated drawdowns during market turmoil.\u003c/p\u003e\u003cp\u003eDirectional analysis (see network graphs, Fig.\u0026nbsp;3and table-5) confirms a hierarchical market structure. Central nodes\u0026mdash;predominantly DeFi blue chips and major altcoins\u0026mdash;function as the primary initiators of risk propagation, with smaller tokens and ETFs largely reacting to these systemic movements. The Total Connectedness Index (Fig.\u0026nbsp;3) quantifies this dynamic, indicating that approximately 80% of the variance across assets is attributable to cross-asset spillovers, while only a minority is due to idiosyncratic shocks. Notably, the Net Spillover plot (Fig.\u0026nbsp;4) reveals distinct asymmetries between transmitters and absorbers, confirming the strategic need to balance risk origins and destinations in portfolio design.\u003c/p\u003e\u003cp\u003eA unique feature of this research is the explicit consideration of carbon risk and environmental impact in portfolio construction. NFTs and DeFi tokens, by virtue of their reliance on energy-intensive blockchain protocols, are associated with substantial carbon emissions. If these emissions persist or intensify, regulatory authorities may be compelled to restrict or ban trading in the most carbon-intensive tokens. This regulatory uncertainty amplifies the need for integrating Carbon ETFs (e.g., CRBN, ICLN, SMOG) within digital asset portfolios\u0026mdash;not just as a financial diversification tool, but also as a forward-looking hedge against carbon risk and future regulatory shocks. Analysis indicates that while carbon and clean energy ETFs exhibit modest spillover to and from digital assets (e.g., SUPERf transmits to PBW, QLCN, CRBN; clean energy ETFs like QLCN also transmit minor shocks to AAVE and Maker), their primary value lies in their relative independence and strong intra-sector cohesion. This finding provides direct support for environmentally conscious investors: portfolios that combine blockchain-based tokens and Carbon ETFs can achieve both sustainability objectives and enhanced resilience against idiosyncratic and regulatory risk.\u003c/p\u003e\u003cp\u003eThe low level of cross-sector spillovers (crypto-to-ETF and vice versa) means meaningful diversification is attainable by combining these asset classes. This is especially pertinent as crypto assets remain highly volatile relative to ETFs (Fig.\u0026nbsp;4, Returns of All Assets Over Time). High own-variance contributors (DAI, USDC) should be strategically weighted as portfolio stabilizers. Central transmitters (Chainlink, BAT, TEZOS) require careful monitoring and potentially smaller allocations to avoid outsized systemic risk. Carbon ETFs should be incorporated for those wishing to hedge environmental risks, anticipate policy changes, and build climate-aligned portfolios.\u003c/p\u003e\u003cp\u003eThis connectedness analysis establishes a quantitative foundation for multi-asset, sustainability-oriented portfolio management. By empirically demonstrating sectoral clustering, the centrality of key transmitters, and the low cross-sector spillover between blockchain assets and carbon ETFs, the results directly support the strategic inclusion of Carbon ETFs for balancing both financial and environmental risk. As NFTs and DeFi tokens continue to generate substantial carbon emissions, the risk of regulatory intervention looms ever larger, reinforcing the need for proactive portfolio diversification. These findings set the stage for further price prediction and regime-shift modeling using SETAR and Hurst exponent approaches, which will be discussed in the following sections to provide a holistic risk management framework.\u003c/p\u003e\u003cp\u003eThe connectedness analysis reveals a highly integrated system of crypto-assets and thematic ETFs, with a Total Connectedness Index of 76.03%. Thematic ETFs, particularly those related to clean and green energy (KGRN, ICLN, QLCN), act as major net transmitters of volatility, highlighting their systemic importance in this specific asset network. On the other hand, several cryptocurrencies, most notably Stacks and the stablecoin DAI, are strong net receivers of shocks. The dynamic analysis underscores that these relationships are not static; the overall system risk and the roles of individual assets fluctuate significantly over time, reacting to changing market conditions. These findings have critical implications for portfolio diversification, risk management, and understanding financial stability in a market that increasingly links traditional thematic investments with digital assets.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research provides a comprehensive analysis of the volatility spillover and connectedness dynamics between a diverse portfolio of cryptocurrencies including NFT and DeFi tokens and thematic Exchange-Traded Funds (ETFs), with a specific emphasis on carbon and clean energy assets. By employing the Diebold-Yilmaz (2012) spillover index and a thorough examination of the assets' statistical properties, this paper offers critical insights for multi-asset portfolio construction, risk management, and sustainable investing (Kilic et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; De Sousa Gabriel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe analysis reveals a tightly integrated system, evidenced by a Total Connectedness Index (TCI) of 76.03%. This indicates that over three-quarters of the volatility in the examined assets is driven by spillovers from others in the network, while less than a quarter stems from asset-specific (idiosyncratic) shocks. This high level of interconnectedness underscores the paramount importance of understanding systemic risk over individual asset behavior (Nadeem et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study identifies clear, asymmetric roles among assets. Thematic ETFs, particularly those related to clean energy and technology (KGRN, ICLN, QLCN), along with key blockchain infrastructure tokens like Chainlink (NET: 30.35) and utility tokens like BAT (NET: 36.95), are the primary net transmitters of volatility. They are the main sources of systemic risk propagation within this ecosystem. In stark contrast, cryptocurrencies such as Stacks (NET: \u0026minus;\u0026thinsp;75.52), WAX (NET: \u0026minus;\u0026thinsp;60.26), and XYO NETWORK (NET: \u0026minus;\u0026thinsp;58.65) are overwhelmingly net receivers. Their volatility is largely a reaction to system-wide movements, positioning them as potential diversifiers against the primary risk drivers. Stablecoins DAI (NET-34.02) and USDC exhibit the highest own-variance contributions (56.91% and 58.23%, respectively), confirming their design as anchors of stability. However, their notable spillover connections, particularly to each other, show they are not entirely immune to systemic contagion.\u003c/p\u003e\u003cp\u003eA pivotal finding is the strong intra-sector connectedness. DeFi tokens (e.g., AAVE, Synthetix) exhibit powerful bidirectional spillovers among themselves, as do the clean energy ETFs (e.g., TAN, ICLN, FAN), which show even higher internal cohesion (8\u0026ndash;12%). Critically, the cross-sector spillovers between the crypto-asset cluster and the ETF cluster are remarkably low, generally remaining below 2%. This creates a distinct risk boundary and presents a clear, quantifiable opportunity for effective diversification.\u003c/p\u003e\u003cp\u003eThe descriptive statistics confirm that the returns of all assets profoundly deviate from normality. Extreme negative skewness in assets like AAVE (\u0026ndash;22.65) and XYO NETWORK (\u0026ndash;43.21), alongside extreme positive skewness in WAX and PBW, points to frequent, large, and asymmetric price movements. Massive excess kurtosis, especially for XYO NETWORK (1887.43) and AAVE (801.09), highlights the prevalence of \"fat tails\" and extreme event risk, rendering traditional risk models based on normal distributions inadequate for this asset space.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Implications for Portfolio Management and Sustainable Investing\u003c/h2\u003e\u003cp\u003eThe low cross-sector spillover between cryptocurrencies and thematic ETFs provides a strong empirical foundation for portfolio diversification. Combining these two asset classes can significantly mitigate risk, as shocks originating in one sector do not readily transmit to the other.\u003c/p\u003e\u003cp\u003eThe reliance of many digital assets on energy-intensive protocols creates a significant, non-financial risk: the potential for regulatory intervention driven by environmental concerns. Including Carbon and Clean Energy ETFs (CRBN, ICLN, SMOG) in a digital asset portfolio serves a dual purpose. Financially, they provide diversification. Strategically, they act as a hedge against future carbon-related regulatory shocks that could negatively impact the crypto market. A sophisticated portfolio strategy should account for each asset's systemic role. The high net spillovers from transmitters like Chainlink, BAT, and key ETFs demand careful risk budgeting and potentially smaller allocations. Conversely, net receivers like Stacks and WAX can be used to balance a portfolio's overall risk profile. The stability of DAI and USDC makes them suitable as core, risk-mitigating holdings.\u003c/p\u003e\u003cp\u003eThis paper systematically maps the architecture of risk transmission across digital and sustainable asset classes. It moves beyond conventional financial analysis to integrate environmental risk as a core portfolio construction variable. By demonstrating the distinct clustering of risk and the low degree of spillover between crypto-assets and carbon-focused ETFs, the research provides a clear, data-driven rationale for building integrated portfolios that are not only financially resilient but also strategically positioned for a future where environmental sustainability and regulatory landscapes are increasingly intertwined.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe authors made contribution to this paper as follows: R.P wrote the methodology and analysis chapters, S.K wrote the introduction and literature review chapters and C.L.M wrote the analysis and conclusion chapters.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We are grateful to the anonymous reviewers for their valuable suggestions and comments on our manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement: The\u003c/strong\u003e data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declared no potential conflicts\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah M, Chowdhury MaF, Ullah GW. 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Resour Policy. 2010;35(3):168\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resourpol.2010.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.resourpol.2010.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Volatility Spillover, Connectedness, Diebold-Yilmaz, Cryptocurrencies, DeFi, Sustainable Finance, Carbon ETFs, Systemic Risk","lastPublishedDoi":"10.21203/rs.3.rs-8195989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8195989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the volatility spillover and connectedness dynamics at the intersection of the rapidly expanding digital asset ecosystem and the sustainable finance sector, motivated by the growing need to manage both financial and environmental regulatory risks. Employing the Diebold-Yilmaz (2012) spillover index framework based on a rolling-window Vector Autoregression (VAR) model, we analyze daily returns of a diverse portfolio of 30 assets from 2020 to 2025. The portfolio includes DeFi tokens (AAVE, Maker), NFTs and utility tokens (WAX, Chiliz), foundational cryptocurrencies, and thematic ETFs focused on clean energy and carbon (ICLN, CRBN). The empirical results reveal a highly interconnected system with a Total Connectedness Index (TCI) of 76.03%. We identify thematic ETFs (KGRN, ICLN) and key infrastructure tokens like Chainlink (NET: 30.35) as the primary net transmitters of volatility. Conversely, cryptocurrencies such as Stacks (NET: \u0026minus;\u0026thinsp;75.52) and the stablecoin DAI (NET: -34.02) are the largest net receivers of systemic shocks. A pivotal finding is the pronounced sectoral clustering within asset classes, alongside remarkably low cross-sector spillover (below 2%) between the digital asset and ETF groups. This informational boundary suggests significant diversification benefits. The findings provide a quantitative basis for constructing resilient portfolios, highlighting the strategic role of Carbon and Clean Energy ETFs not only for financial diversification but also as a crucial hedge against the inherent carbon-related regulatory risks in the digital asset market.\u003c/p\u003e","manuscriptTitle":"Volatility Connectedness Between Digital Assets and Sustainable Finance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 06:39:33","doi":"10.21203/rs.3.rs-8195989/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-24T12:39:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-21T12:20:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327597006429749317712643158432061008787","date":"2025-12-14T12:25:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T10:03:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296765525950402990950563345601771134998","date":"2025-12-01T07:48:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T04:41:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-01T03:18:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T06:50:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T06:46:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-11-24T17:27:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10e25ea2-163c-44c6-a055-67349f31b2b8","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T11:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 06:39:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8195989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8195989","identity":"rs-8195989","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}