The Crypto-Equity Nexus: A Novel Simulation of Risk Transmission Channels

The Crypto-Equity Nexus: A Novel Simulation of Risk Transmission Channels | 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 The Crypto-Equity Nexus: A Novel Simulation of Risk Transmission Channels Ozan Nadirgil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9172803/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction of cryptocurrency markets offered unique confidentiality and hedging benefits to investors, since they have been rapidly integrated into the financial system through forming substantial long- and short-term interdependencies with equity markets. Previous studies present contradicting and ambigious conclusions about the financial contagion between the equity and crypto markets, nonetheless they do not adequately pose light on the impacts of global events on the relationship between these markets. To address these concerns, this research examines the volatility spillovers between the equity and Decentralized Finance (DeFi) markets through simulating the dependencies with the aim of eliminating the bias and drawbacks of the traditional models by appling an optimized and original analytical framework composed of Deep Neural Network (DNN) and Time Varying Parameters Vector Auto Regression (TVP-VAR) models. Results identify substantial transmission from Bitcoin (BTC), Nasdaq 100 (NASDAQ), Shanghai Stock Exchange (SSE), and New York Stock Exchange (NYSE) to BNB, Euronext, Tether (TET), and Ethereum (ETH) and reflect the significant impact of the global important events on the financial spillovers. Model assessment results confirm the robust accuracy, fitness, reliability, and validity of the model, as well as the success of key parameter optimization. Computational Finance DeFi Markets Equity Markets Financial Contagion Risk Transmission Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background 1.1. Introduction In the neoteric age, financial markets are excessively integrated and risks are highly contagious. Consequently, comprehension of financial risk transmissions has an utmost priority for investors to formulize advanced hedging and diversification strategies and for policy makers to predict the financial crises and enhance the economic stability. Launch of Bitcoin (BTC) in 2009, commenced an alternative and fresh environment for investors. Specifically, independent and decentralized dispositions of the decentralized financial assets (DeFi) enhanced the privacy and security of investments and their unique functional features bolstered the market capitalization of the crypto assets, which currently encompasses around 24.000 cryptocurrencies in circulation. Owing to their unique features, DeFi assets possess the potential to serve as thriving hedgers and diversifiers against the traditional asset types, such as stocks, bonds, commodities and funds. In conclusion, exploring the risk transmission mechanisms between the crypto currency and traditional markets is expected to deepen the comprehension of their hedging potentials and facilitate developing advanced portfolio allocation strategies, in addition to enhancing the capabilities of policy makers to deal with the risks of financial contagion (Nadirgil, 2024). 1.2. Related Literature Former research investigates the interdependencies between the stock and DeFi markets from various standpoints, nevertheless, they are constrained by narrow datasets and the application of deterministic methods. For instance, Bouri et al. (2020) used Wavelet Coherence and found hedging potential of BTC against the equity markets and Khalfaoui et al. (2023) searched the connectedness among BRICS and DeFi markets by applying a Quantile Vector Autoregression (QVAR) method. In addition, Ahmed et al. (2023) researched the relationships between the crypto currency market and the S&P500 index by implementing a VAR model and discovered long and short-run dependencies in their network. Furthermore, Wu et al. (2019) analyzed the spillovers across different sectors in Chinese stock markets by using a graph theory and identified the time varying structure of the spillovers and Khalfaoui et al. (2024) used QVAR to examine the spillovers among the crypto coins (Bitcoin, Ethereum, Litecoin), stock markets, and global uncertainty indices, and determined a tendency of net spillovers over quantiles. Some further studies adopted dynamic models to capture time varying dependencies across the stock and crypto markets. In this context, Younis et al. (2024) applied Time-Varying Parameter Vector Autoregression (TVP-VAR) model to reveal the connections between and within the decentralized finance assets (DeFi), equity markets and bank stocks and identified important internal and external dependencies among the markets throughout the COVID-19 period. Moreover, Wang et al. (2024) executed Quantile Frequency Connectedness model to examine the connections among renewable energy, energy tokens, and crypto markets, and their results indicate that crypto coins are connected to energy tokens, while the renewable energy markets are internally connected. In addition, Yousaf et al. (2024) preferred TVP-VAR to examine the spillovers between the stocks and green coins and found the rise of the total spillover and hedging costs during the COVID-19 pandemic and the Russia-Ukraine war, and Abdullah et al. (2025) explored risk spillovers among cryptocurrencies, FinTech stocks, and traditional assets by using a time domain quantile connectedness method, then identified FinTech stocks as net transmitters and bonds as net receivers. Moreover, Bibi et al. (2025) examined the return transmissions across an ESG index and crypto coins by using the TVP-VAR and concluded that negative connectedness surpasses the positive one, and Attarzadeh et al. (2024) analyzed the dynamic and temporal dependencies across the DeFi, energy, gold, and stock markets and summarized that the relationships of gold and BTC with others are weaker during the crises. Eventually, Zhao and Zhang (2024) searched for return dependencies between BTC and major international stock markets by implementing a TVP-VAR model and concluded that BTC was not a safe haven asset for global stock markets during the post-Covid-19 period. Finally, Nadirgil (2025) investigated risk transmission inside a large portfolio of assets from equity, commodity, and DeFi markets by developing original and fine-tuned versions of the TVP-VAR method. In conclusion, methodologies of the former research evolved from deterministic and stationary methods, such as VAR and GARCH to various versions of the TVP-VAR model to improve its nonlinear and dynamic capabilities. In particular, TVP-VAR models are extremely useful and flexible tools with their rolling window features in unraveling the asymmetric and dynamic transmissions in different time horizons, nonetheless they also possess some serious drawbacks. First of all, they are based on the assumption of stationarity and prone to the serious negative consequences of overfitting, underfitting, and structural bias. In addition, while they are able to capture the dynamic dependencies across the selected time frames, they operate on the assumption of linearity within the rolling window, as a result they do not possess the capability to fully capture the complex and nonlinear dynamics of the DeFi and equity markets. 1.3. Originality Crypto currency market is an astonishingly flourishing and dynamic market, therefore crypto currency price data is inherently heteroscedastic, nonlinear and complex. In the light of the literature, deterministic and linear methods are not adequate to unveil the complex interdependencies of the DeFi markets with others, which should be analyzed by advanced heteroscedastic nonlinear methods. In this context, this study aims to analyze the asymmetric and nonlinear risk transmissions across the biggest crypto currency and stock markets by introducing an original and robust methodology incorporated with deep and machine learning models combined with novel optimization and validation methods. Previous studies examined the interdependencies among various equity and DeFi markets, nevertheless their methods fall a short in unveiling the nonlinear dependencies due to the implementation of deterministic and stationary methods. Despite the time varying features of the TVP-VAR model, it is a regression based and stationary model, hence fails to capture the non-linear transmission dynamics inside the rolling window size. Particularly, in case of heteroscedasticity, drawbacks of these models are expected to be enhanced in comparing the results across the selected time periods. Nevertheless, methodology of this study is equipped with original nonlinear and nonparametric features through eliminating the structural bias and stationarity stemming from the TVP-VAR model through simulating the interdependencies by incorporating a DNN architecture. In conclusion, originality of this research lies in its original and innovative methodology employing a DNN-TVP-VAR method to examine the linear, non-linear, and asymmetric transmissions inside a geographically diversified dataset, including the biggest stock and DeFi markets. Key parameters of the model, including the learning rate, number of epochs, batch size, sliding window size, and the number of lags are fine tuned by using automated grid search techniques to maximize the efficency of the model. 2. Data and Methodology 2.1. Dataset Dataset includes the daily values of the biggest stock markets around the world, containing Nasdaq 100 (NASDAQ), Euronext 100 (EURONEXT), New York Stock Exchange 100 (NYSE), Financial Times Stock Exchange 100 (FTSE), and Shanghai Stock Exchange 100 (SSE), combined with the daily prices of the 4 biggest crypto currency markets, including Bitcoin (BTC), Tether (TET), Ethereum (ETH), and BNB. The time frame of the study is selected to cover the period from January 2019 to January 2024 to reflect the effects of important global and regional events, such as the Covid-19, Brexit, and the Ukrainian war on the total and pairwise transmissions. Detailed description of the entire dataset is provided in Table 1 . Table 1 Dataset Variable Code INDEX NASDAQ 100 NASDAQ Euronext 100 EURONEXT Financial Times Stock Exchange 100 FTSE New York Stock Exchange 100 NYSE Shanghai Stock Exchange 100 SSE CRYPTO CURRENCIES Bitcoin BTC Ethereum ETH Tether TET BNB BNB 2.2. Deep Neural Network Time Varying Vector Auto Regression Model (DNN-TVP-VAR) Considering the bias and drawbacks of single TVP-VAR variances, this research employs an original approach designed by combining optimized DNN and TVP-VAR models. According to the methodology displayed in Fig. 1 , dataset is initially cleaned, analyzed, normalized, and scaled. After applying an Augmented Dickey-Fuller (ADF) test, all identified unit roots are removed by first order differencing. Subsequently, a VAR model with an optimized number of lags is applied to extract the residuals and subsequently residuals of the first step are simulated in a DNN architecture, in which the batch size, number of epochs, and the learning rate are best-tuned by grid search functions. Eventually, GFEVD values are calculated for the specified forecast horizon based on the simulated residuals generated by ensuring that the spillover analysis reflects the simulated dynamics. In particular, a new TVP-VAR approach is applied on the simulated residuals of the DNN model by using a rolling window to capture the dynamic and nonlinear dependencies inside the dataset. At the final stage, total spillovers and the pairwise spillovers are computed by using the GFEVD values, and finally all results are disseminated by a total dynamic transmissions graph, pair-wise transmissions table and graphs, and a transmission network diagram. At the assessment stage, accuracy, fitness, precision, and the recall measures of the new approach are measured by the performance metrics described in section 3. Ultimately, statistical significances of the variations across the accuracy and reliability performances of the model on the subsets and the degree of overfitting are assessed by bootstrapping tests. Considering the complex, non-linear, dynamic features of the data, DNN-TVP-VAR model is expected to detect the nonlinear and asymmetric relationships across the selected variables by utilizing its unique rolling window and randomizer features. In addition, dynamic structure of the new method is anticipated to provide important contributions to the analysis of the evolving market environments, such as the DeFi and stock markets, in which the transmission dynamics transform frequently. Finally, models’ performance is escalated and the drawbacks are eliminated through optimizing the key parameters, cross fold validation, and practicing the model on the subsets before deployment. Eventually, success of the model and the mitigation of drawbacks are verified by bootstrapping method. Consequently, original and hybrid methodology of this research is anticipated to bring valuable contributions through enhancing the capabilities of the traditional models by mitigating their assumptions and bias. 3. Findings and Assessment 3.1. Optimization Results As the performances of the developed models are depply rely on key parameters, key parameters of the hybrid model, including the batch size, number of epochs, learning rate, number of lags, and the dynamic window size are best-tuned by grid search method, which is a powerful optimization technique to explore a predefined set of hyperparameter combinations and identify the optimal configurations of a model. By exhaustively testing each combination, grid search executes a thorough and rigorous search of the determined range and it is particularly effective for the small datasets. This method is preferred in this study because of its simplicity and reproducibility, as it ensures that all possible combinations are evaluated without a bias. As a result, key parameters are fine-tuned by grid search and results are depicted in Table 2 . Table 2 Optimized Parameters Parameter Name Model Trial Best-Tuned Value Batch Size DNN 100 32 Number of Epochs DNN 10 100 Learning Rate DNN 100 0.05 Rolling Window Size TVP-VAR 100 30 Lags TVP-VAR 100 1 3.2. Total and Pairwise Dynamic Transmissions Table 3 presents the total transmission results among the selected variables and shed light into the flow of risk transmissions inside the network. Results identify NASDAQ, BTC, NYSE, and SSE as the substantial risk transmitters, whereas BNB, EURONEXT, TET, and ETH are exposed to highest risk transmission within the selected dataset. On contrary to the previous studies, findings suggest that cryptocurrencies, excluding BTC, do not possess the potential to serve as robust hedgers or diversifiers for stock-based portfolios, since they are significantly influenced by the shocks in capital markets and weakly connected to each other. In addition, results diversify the Euronext from the other stock markets and converge it into the crypto currency group as a net receiver of risk transmissions. Table 3 Total Dynamic Transmissions NASDAQ NYSE FTSE EURONEXT SSE BTC ETH TET BNB FROM NASDAQ 0 0.129801 0.10442 0.046557 0.101848 0.174324 0.043395 0.054476 0.032217 0.687039 NYSE 0.253367 0 0.101727 0.060519 0.107132 0.160095 0.067459 0.066605 0.044471 0.861375 FTSE 0.242754 0.137825 0 0.064045 0.107015 0.157923 0.07255 0.066854 0.048043 0.897008 EURONEXT 0.239683 0.137988 0.103255 0 0.10698 0.157162 0.073619 0.067284 0.049344 0.935314 SSE 0.238527 0.137813 0.103006 0.064883 0 0.15686 0.074262 0.067564 0.050065 0.89298 BTC 0.237839 0.137483 0.103025 0.065079 0.106884 0 0.074649 0.067574 0.050716 0.843249 ETH 0.237299 0.137188 0.103039 0.06516 0.106703 0.156818 0 0.067539 0.05132 0.925066 TET 0.23694 0.136886 0.102917 0.065228 0.106604 0.156946 0.075205 0 0.051776 0.932502 BNB 0.23665 0.136597 0.102883 0.065315 0.106525 0.156988 0.075503 0.067486 0 0.947947 TO 1.923059 1.091579 0.824272 0.496787 0.84969 1.277116 0.556643 0.525383 0.37795 NET 1.236021 0.404541 0.137233 -0.19025 0.162651 0.590077 -0.1304 -0.16166 -0.30909 Figure 2 illustrates how the global events, including the economic downturn after Brexit, onsets of the two Covid-19 pandemic waves, and the beginning of the Ukrainian war in late February 2022, transformed the total dynamic transmissions over time. According to the figure, total dynamic transmission experienced a sharp decline during the economic turmoil in the second half of 2019, which is followed by a substantial spike in early 2020 instigated by the collapse of the financial markets following the first covid-19 wave. This is succeeded by a local peak in March 2021, corresponding to the second pandemic wave, and a substantial rise starts from the beginning of 2022. Figure 4 illustrates the pairwise directional transmission plots from the first variable to the second. In particular, figure highlights that pairwise transmissions of the stock markets and BTC are similar in pattern with the total transmission graph on reflecting the effects of the global events, while the pairwise transmissions of the remaining cryptocurrencies demonstrate only the influence of the first pandemic wave. 3.3. Model Assessment Results Table 4 shows the model`s performance scores explained in equations 1 to 5 for both subsets combined with the bootstrapping test results to assess the reliability, accuracy, and precision aspects of the model and to evaluate the elimination of drawbacks. MAPE, MSE, and MAE scores reflect the accuracy and precision capabilities of the model, whereas EVS, and the \(\:{R}^{2}\) provide reliable insights about the model`s fitness on the dataset. In conclusion, results are presented collectively and refer to the model's reliability and robustness in capturing the dynamic and nonlinear transmission inside the network. Considering the model`s fitness, exceptionally high EVS scores indicate that more than 99% of the prediction variance inside the network is captured by the model. Similarly, R² scores confirm the robust accuracy by illustrating that 99% of the predicted residuals are coming from the original values. In addition, MAPE values provide a comparative assessment of the models forecasting performance by expressing the deviations of predictions as a percentage, hence consistently low MAPE scores highlight the strong prediction accuracy of the model for each subset. Finally, comparing the bootstrapping test results, same upper and lower bound confidence intervals (CI) of each metric on different subsets highlight that the performance score disparities across the subsets are statistically not significant, therefore there is no substantial sign of overfitting and the model is absolutely reliable. Additionally, Fig. 3 proposes another perspective for the model`s robust prediction performance by exhibiting the actual and predicted plots of the residuals and indicates that most remarkable deviations are observed for the NASDAQ index, which has the highest net transmissions inside the network. Table 4 Assessment and Validity Scores MSE : Train Test 1042.82 433.97 MSE CI Lower 0.148563 0.148563 MSE CI Upper 0.235896 0.235896 MAPE : 0.002 0.0019 MAPE CI Lower 0.004208 0.004208 MAPE CI Upper 0.004127 0.004127 MAE : 8.8308 7.5031 MAE CI Lower 0.218536 0.218536 MAE CI Upper 0.278954 0.278954 EVS : 0.99679 0.99659 EVS CI Lower 0.987583 0.987583 EVS CI Upper 0.983561 0.983561 R² : 0.99678 0.99652 R2 CI Lower 0.987985 0.987985 R2 CI Upper 0.986528 0.986528 4. Conclusions This research explores the volatility connectedness in a geographically diversified and extensive portfolio of variables, containing the biggest stock and DeFi markets by deploying an original and fine-tuned DNN-TVP-VAR model. Briefly, model is developed by incorporating novel DNN and TVP-VAR structures to eliminate the drawbacks of the parametric and stationary methods through simulating the dependencies and optimizing the model parameters to encapsulate the asymmetric, dynamic, and non-linear transmissions. Model parameters, including the learning rate, batch size, number of epochs, number of lags, and rolling window size are best tuned through original grid search mechanisms, and the model is deployed by using the final results. Last of all, model`s strength and the successful elimination of drawbacks are validated by applying novel bootstrapping tests on the performance score disparities of the model across the subsets. Total transmission results identify substantial transmissions from NASDAQ, BTC, NYSE, and SSE to BNB, EURONEXT, TET, and ETH, and illustrate the influence of the important events on the financial contagion, involving Brexit, first and second waves of Covid-19, and the military conflicts between Russia and Ukraine. In particular, total transmission volume sharply declines during the economic downturn caused by Brexit and substantial rises in early 2020 due to the first Covid-19 wave. Subsequently, it reaches a local peak during the second pandemic wave in March 2021, and finally peaks at the beginning of the Ukrainian war in February 2022. Furthermore, pairwise transmission graphs of the stock markets and BTC illustrate similar patterns with the total transmissions graph and explicitly reflect the impacts of the examined global events. Finally, model assessment indicates that high EVS and R² scores imply the robust fitness and precision performance, and the exceptionally low MAPE values signify the excellent accuracy of the model. Ultimately, bootstrapping findings confirm the strength of the new approach and the achievement of key parameter optimization. Consequently, this study offers a robust and original approach to investigate the risk transmissions inside the neoteric financial system through simulating the dependencies and capturing asymmetric, dynamic, and nonlinear risk transmissions across the biggest equity and DeFi markets, while eliminating the drawbacks of the integrated models by key parameter optimization. As a result, findings of this study provide invaluable insights to advance the comprehension of financial risk spillover dynamics across the DeFi and equity markets, in addition its original, reliable, and sturdy methodology is anticipated to be adopted by the future studies in addressing new research questions. References Abdullah M, Chowdhury MAF, Ullah GW (2025) Asymmetric tail risk dynamics, efficiency and risk spillover among FinTech stocks, cryptocurrencies and traditional assets. Glob Financ J, 101082 Ahmed MF, Sarkodie SA, Leirvik T (2023) Mutual coupling between stock market and cryptocurrencies, Heliyon, Volume 9, Issue 5, 2023, e16179, ISSN 2405–8440. https://doi.org/10.1016/j.heliyon.2023.e16179 Attarzadeh A, Isayev M, Irani F (2024) Dynamic interconnectedness and portfolio implications among cryptocurrency, gold, energy, and stock markets: A TVP-VAR approach. Sustainable Futures 8:100375 Bibi R, Gulzar S, Shahzad SJH (2025) ESG leaders and crypto currency market: Asymmetric TVP-VAR connectedness and investment approaches. Res Int Bus Finance 76:102833 Bouri E, Shahzad SJH, Roubaud D, Kristoufek L, Lucey B (2020) Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. Q Rev Econ Finance 77:156–164 Khalfaoui R, Hammoudeh S, Rehman MZ (2023) Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network. Emerg Markets Rev. 54, 2023, 101002, ISSN 1566 – 0141 https://doi.org/10.1016/j.ememar.2023.101002 Nadirgil O (2023) The relationship between the contaminating industries and the European carbon price, machine learning approach. J Clean Prod 426:139131 Nadirgil O (2025) Dynamic Transmissions Between Green and Technology Stocks, ETFs, Commodities, Crypto and Fiat Currencies Throughout the Global Turbulences. J Clean Prod, 145002 Wang X, Liu J, Xie Q (2024) Quantile frequency connectedness between energy tokens, crypto market, and renewable energy stock markets. Heliyon, 10(3) Wu F, Zhang D, Zhang Z (2019) Connectedness and risk spillovers in China’s stock market: A sectoral analysis, Economic Systems, Volume 43, Issues 3–4, 2019, 100718, ISSN 0939–3625. https://doi.org/10.1016/j.ecosys.2019.100718 Younis I, Gupta H, Du AM, Shah WU, Hanif W (2024) Spillover dynamics in DeFi, G7 banks, and equity markets during global crises: A TVP-VAR analysis. Res Int Bus Finance, 102405 Yousaf I, Cui J, Ali S (2024) Dynamic spillover between green cryptocurrencies and stocks: A portfolio implication. Int Rev Econ Finance 96:103661 Zhao J, Zhang T (2023) Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach. Finance Res Lett 58:104342 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9172803","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609101667,"identity":"57e423da-eef7-47cd-aa95-fdd039345221","order_by":0,"name":"Ozan Nadirgil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJACiQQGBh4+9gZStbDxHAAyE4jVAiLYQBqJ0sIv3fzwxsM9tTJskm+Pffj4wyaPQezwAbxaJOccM7ZIeHach006L3nmjIS0YgbpNPx2GdxIMJNIOHAMqCXHmJkn4XBig3SOAQEt6d8gWiTPwLTkfyCgJQdkSw0PmwQP3Ba8OhgkZ+QUWyQcOAAM5LxkxhlpaYlt0mn4HcYvkb7x5o8Ddfb87GcPM3ywsUnsl05+gN8aCDgMxDwQJhsx6oGgDqFlFIyCUTAKRgE6AADt70EVFRv4vQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-5584-0488","institution":"Independent","correspondingAuthor":true,"prefix":"","firstName":"Ozan","middleName":"","lastName":"Nadirgil","suffix":""}],"badges":[],"createdAt":"2026-03-19 20:26:19","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9172803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9172803/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728242,"identity":"8c74b6d9-68c6-4da8-911c-0e7c91c5bcc9","added_by":"auto","created_at":"2026-03-30 11:11:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117349,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological Framework\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9172803/v1/8b52b6ed7492513912bb843f.jpg"},{"id":105633100,"identity":"d63b988f-f875-404f-9074-84cb26494d18","added_by":"auto","created_at":"2026-03-28 13:19:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59890,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Dynamic Transmissions\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9172803/v1/f569d1b8fcc23fbccf45064d.jpg"},{"id":105728489,"identity":"a59dd60c-195c-4795-bd2e-f784abf16c09","added_by":"auto","created_at":"2026-03-30 11:12:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97605,"visible":true,"origin":"","legend":"\u003cp\u003eActual vs. Predicted Residuals\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9172803/v1/1796d225c2da93f91389af8b.jpg"},{"id":105633098,"identity":"62f13572-e992-4a76-8241-3e74562fd174","added_by":"auto","created_at":"2026-03-28 13:19:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1076833,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise Transmissions\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9172803/v1/07670fd3d78c37ec8fbe1184.jpg"},{"id":106401694,"identity":"fe62a1cf-3984-4781-8b8f-824c146f32a4","added_by":"auto","created_at":"2026-04-08 09:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1912159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9172803/v1/72f3ba34-cf68-4a33-9b51-1581df0e0450.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Crypto-Equity Nexus: A Novel Simulation of Risk Transmission Channels\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.\tBackground","content":"\u003cp\u003e\u003cstrong\u003e1.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Introduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the neoteric age, financial markets are excessively integrated and risks are highly contagious. Consequently, comprehension of financial risk transmissions has an utmost priority for investors to formulize advanced hedging and diversification strategies and for policy makers to predict the financial crises and enhance the economic stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLaunch of Bitcoin (BTC) in 2009, commenced an alternative and fresh environment for investors. Specifically, independent and decentralized dispositions of the decentralized financial assets (DeFi) enhanced the privacy and security of investments and their unique functional features bolstered the market capitalization of the crypto assets, which currently encompasses around 24.000 cryptocurrencies in circulation.\u003c/p\u003e\n\u003cp\u003eOwing to their unique features, DeFi assets possess the potential to serve as thriving hedgers and diversifiers against the traditional asset types, such as stocks, bonds, commodities and funds. In conclusion, exploring the risk transmission mechanisms between the crypto currency and traditional markets is expected to deepen the comprehension of their hedging potentials and facilitate developing advanced portfolio allocation strategies, in addition to enhancing the capabilities of policy makers to deal with the risks of financial contagion (Nadirgil, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Related Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormer research investigates the interdependencies between the stock and DeFi markets from various standpoints, nevertheless, they are constrained by narrow datasets and the application of deterministic methods. For instance, Bouri et al. (2020) used Wavelet Coherence and found hedging potential of BTC against the equity markets and Khalfaoui et al. (2023) searched the connectedness among BRICS and DeFi markets by applying a Quantile Vector Autoregression (QVAR) method. In addition, Ahmed et al. (2023) researched the relationships between the crypto currency market and the S\u0026amp;P500 index by implementing a VAR model and discovered long and short-run dependencies in their network. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, Wu et al. (2019) analyzed the spillovers across different sectors in Chinese stock markets by using a graph theory and identified the time varying structure of the spillovers and \u0026nbsp;Khalfaoui et al. (2024) used QVAR to examine the spillovers among the crypto coins (Bitcoin, Ethereum, Litecoin), \u0026nbsp; stock markets, and global uncertainty indices, and determined a tendency of net spillovers over quantiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSome further studies adopted dynamic models to capture time varying dependencies across the stock and crypto markets. In this context, Younis et al. (2024) applied Time-Varying Parameter Vector Autoregression (TVP-VAR) model to reveal the connections between and within the decentralized finance assets (DeFi), equity markets and bank stocks and identified important internal and external dependencies among the markets throughout the COVID-19 period. Moreover, Wang et al. (2024) executed Quantile Frequency Connectedness model to examine the connections among renewable energy, energy tokens, and crypto markets, and their results indicate that crypto coins are connected to energy tokens, while the renewable energy markets are internally connected. In addition, Yousaf et al. (2024) preferred TVP-VAR to examine the spillovers between the stocks and green coins and found the rise of the total spillover and hedging costs during the COVID-19 pandemic and the Russia-Ukraine war, and Abdullah et al. (2025) explored risk spillovers among cryptocurrencies, FinTech stocks, and traditional assets by using a time domain quantile connectedness method, then identified FinTech stocks as net transmitters and bonds as net receivers. Moreover, Bibi et al. (2025) examined the return transmissions across an ESG index and crypto coins by using the TVP-VAR and concluded that negative connectedness surpasses the positive one, and Attarzadeh et al. (2024) analyzed the dynamic and temporal dependencies across the DeFi, energy, gold, and stock markets and summarized that the relationships of gold and BTC with others are weaker during the crises. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEventually, Zhao and Zhang (2024) searched for return dependencies between BTC and major international stock markets by implementing a TVP-VAR model and concluded that BTC was not a safe haven asset for global stock markets during the post-Covid-19 period. Finally, Nadirgil (2025) investigated risk transmission inside a large portfolio of assets from equity, commodity, and DeFi markets by developing original and fine-tuned versions of the TVP-VAR method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, methodologies of the former research evolved from deterministic and stationary methods, such as VAR and GARCH to various versions of the TVP-VAR model to improve its nonlinear and dynamic capabilities. In particular, TVP-VAR models are extremely useful and flexible tools with their rolling window features in unraveling the asymmetric and dynamic transmissions in different time horizons, nonetheless they also possess some serious drawbacks. First of all, they are based on the assumption of stationarity and prone to the serious negative consequences of overfitting, underfitting, and structural bias. In addition, while they are able to capture the dynamic dependencies across the selected time frames, they operate on the assumption of linearity within the rolling window, as a result they do not possess the capability to fully capture the complex and nonlinear dynamics of the DeFi and equity markets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3. Originality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCrypto currency market is an astonishingly flourishing and dynamic market, therefore crypto currency price data is inherently heteroscedastic, nonlinear and complex. In the light of the literature, deterministic and linear methods are not adequate to unveil the complex interdependencies of the DeFi markets with others, which should be analyzed by advanced heteroscedastic nonlinear methods. In this context, this study aims to analyze the asymmetric and nonlinear risk transmissions across the biggest crypto currency and stock markets by introducing an original and robust methodology incorporated with deep and machine learning models combined with novel optimization and validation methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies examined the interdependencies among various equity and DeFi markets, nevertheless their methods fall a short in unveiling the nonlinear dependencies due to the implementation of deterministic and stationary methods. Despite the time varying features of the TVP-VAR model, it is a regression based and stationary model, hence fails to capture the non-linear transmission dynamics inside the rolling window size. Particularly, in case of heteroscedasticity, drawbacks of these models are expected to be enhanced in comparing the results across the selected time periods. Nevertheless, methodology of this study is equipped with original nonlinear and nonparametric features through eliminating the structural bias and stationarity stemming from the TVP-VAR model through simulating the interdependencies by incorporating a DNN architecture. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, originality of this research lies in its original and innovative methodology employing a DNN-TVP-VAR method to examine the linear, non-linear, and asymmetric transmissions inside a geographically diversified dataset, including the biggest stock and DeFi markets. Key parameters of the model, including the learning rate, number of epochs, batch size, sliding window size, and the number of lags are fine tuned by using automated grid search techniques to maximize the efficency of the model. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"2. Data and Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Dataset\u003c/h2\u003e \u003cp\u003eDataset includes the daily values of the biggest stock markets around the world, containing Nasdaq 100 (NASDAQ), Euronext 100 (EURONEXT), New York Stock Exchange 100 (NYSE), Financial Times Stock Exchange 100 (FTSE), and Shanghai Stock Exchange 100 (SSE), combined with the daily prices of the 4 biggest crypto currency markets, including Bitcoin (BTC), Tether (TET), Ethereum (ETH), and BNB. The time frame of the study is selected to cover the period from January 2019 to January 2024 to reflect the effects of important global and regional events, such as the Covid-19, Brexit, and the Ukrainian war on the total and pairwise transmissions. Detailed description of the entire dataset is provided in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eINDEX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNASDAQ 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNASDAQ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuronext 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEURONEXT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial Times Stock Exchange 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFTSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew York Stock Exchange 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNYSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShanghai Stock Exchange 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCRYPTO CURRENCIES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBitcoin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthereum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTether\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBNB\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Deep Neural Network Time Varying Vector Auto Regression Model (DNN-TVP-VAR)\u003c/h2\u003e \u003cp\u003eConsidering the bias and drawbacks of single TVP-VAR variances, this research employs an original approach designed by combining optimized DNN and TVP-VAR models.\u003c/p\u003e \u003cp\u003eAccording to the methodology displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, dataset is initially cleaned, analyzed, normalized, and scaled. After applying an Augmented Dickey-Fuller (ADF) test, all identified unit roots are removed by first order differencing. Subsequently, a VAR model with an optimized number of lags is applied to extract the residuals and subsequently residuals of the first step are simulated in a DNN architecture, in which the batch size, number of epochs, and the learning rate are best-tuned by grid search functions. Eventually, GFEVD values are calculated for the specified forecast horizon based on the simulated residuals generated by ensuring that the spillover analysis reflects the simulated dynamics. In particular, a new TVP-VAR approach is applied on the simulated residuals of the DNN model by using a rolling window to capture the dynamic and nonlinear dependencies inside the dataset. At the final stage, total spillovers and the pairwise spillovers are computed by using the GFEVD values, and finally all results are disseminated by a total dynamic transmissions graph, pair-wise transmissions table and graphs, and a transmission network diagram.\u003c/p\u003e \u003cp\u003eAt the assessment stage, accuracy, fitness, precision, and the recall measures of the new approach are measured by the performance metrics described in section 3. Ultimately, statistical significances of the variations across the accuracy and reliability performances of the model on the subsets and the degree of overfitting are assessed by bootstrapping tests.\u003c/p\u003e \u003cp\u003eConsidering the complex, non-linear, dynamic features of the data, DNN-TVP-VAR model is expected to detect the nonlinear and asymmetric relationships across the selected variables by utilizing its unique rolling window and randomizer features. In addition, dynamic structure of the new method is anticipated to provide important contributions to the analysis of the evolving market environments, such as the DeFi and stock markets, in which the transmission dynamics transform frequently. Finally, models\u0026rsquo; performance is escalated and the drawbacks are eliminated through optimizing the key parameters, cross fold validation, and practicing the model on the subsets before deployment. Eventually, success of the model and the mitigation of drawbacks are verified by bootstrapping method. Consequently, original and hybrid methodology of this research is anticipated to bring valuable contributions through enhancing the capabilities of the traditional models by mitigating their assumptions and bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Findings and Assessment","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Optimization Results\u003c/h2\u003e \u003cp\u003eAs the performances of the developed models are depply rely on key parameters, key parameters of the hybrid model, including the batch size, number of epochs, learning rate, number of lags, and the dynamic window size are best-tuned by grid search method, which is a powerful optimization technique to explore a predefined set of hyperparameter combinations and identify the optimal configurations of a model. By exhaustively testing each combination, grid search executes a thorough and rigorous search of the determined range and it is particularly effective for the small datasets. This method is preferred in this study because of its simplicity and reproducibility, as it ensures that all possible combinations are evaluated without a bias. As a result, key parameters are fine-tuned by grid search and results are depicted in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eOptimized Parameters\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBest-Tuned Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Epochs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRolling Window Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTVP-VAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLags\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTVP-VAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\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\u003e3.2. Total and Pairwise Dynamic Transmissions\u003c/h2\u003e \n\u003cp\u003eTable 3 presents the total transmission results among the selected variables and shed light into the flow of risk transmissions inside the network. Results identify NASDAQ, BTC, NYSE, and SSE as the substantial risk transmitters, whereas BNB, EURONEXT, TET, and ETH are exposed to highest risk transmission within the selected dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn contrary to the previous studies, findings suggest that cryptocurrencies, excluding BTC, do not possess the potential to serve as robust hedgers or diversifiers for stock-based portfolios, since they are significantly influenced by the shocks in capital markets and weakly connected to each other. In addition, results diversify the Euronext from the other stock markets and converge it into the crypto currency group as a net receiver of risk transmissions.\u003c/p\u003e\n\u003cp\u003eTable 3 Total Dynamic Transmissions\u003c/p\u003e\n\u003cdiv style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:justify;text-indent:14.4pt;line-height:150%;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\n \u003ctable style=\"border: none;border-collapse: collapse;margin-left: 6.75pt;margin-right: 6.75pt;width: 700px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.6pt;border-top: 1pt solid black;border-left: 1pt solid black;border-bottom: none;border-right: none;background: rgb(218, 238, 243);padding: 0cm 5.4pt;height: 31.4pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:left;text-indent: 0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.25pt;border-top: 1pt solid black;border-left: none;border-bottom: 1pt solid black;border-right: none;background: rgb(218, 238, 243);padding: 0cm 5.4pt;height: 31.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cstrong\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003eNASDAQ\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.3pt;border-top: 1pt solid black;border-left: none;border-bottom: 1pt solid black;border-right: none;background: rgb(218, 238, 243);padding: 0cm 5.4pt;height: 31.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cstrong\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003eNYSE\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: 1pt solid black;border-left: none;border-bottom: 1pt solid black;border-right: none;background: rgb(218, 238, 243);padding: 0cm 5.4pt;height: 31.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cstrong\u003e\u003cspan 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style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e0.162651\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.25pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;background: rgb(254, 201, 126);padding: 0cm 5.4pt;height: 46.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e0.590077\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.3pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;background: rgb(166, 209, 126);padding: 0cm 5.4pt;height: 46.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e-0.1304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;background: rgb(154, 206, 126);padding: 0cm 5.4pt;height: 46.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e-0.16166\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.3pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;background: rgb(99, 190, 123);padding: 0cm 5.4pt;height: 46.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e-0.30909\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.3pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 0cm 5.4pt;height: 46.45pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:center;text-indent:0cm;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;margin:0cm;'\u003e\u003cspan style='font-size:12px;line-height:200%;font-family:\"Calibri\",sans-serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates how the global events, including the economic downturn after Brexit, onsets of the two Covid-19 pandemic waves, and the beginning of the Ukrainian war in late February 2022, transformed the total dynamic transmissions over time. According to the figure, total dynamic transmission experienced a sharp decline during the economic turmoil in the second half of 2019, which is followed by a substantial spike in early 2020 instigated by the collapse of the financial markets following the first covid-19 wave. This is succeeded by a local peak in March 2021, corresponding to the second pandemic wave, and a substantial rise starts from the beginning of 2022.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the pairwise directional transmission plots from the first variable to the second. In particular, figure highlights that pairwise transmissions of the stock markets and BTC are similar in pattern with the total transmission graph on reflecting the effects of the global events, while the pairwise transmissions of the remaining cryptocurrencies demonstrate only the influence of the first pandemic wave.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Model Assessment Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the model`s performance scores explained in equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to \u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for both subsets combined with the bootstrapping test results to assess the reliability, accuracy, and precision aspects of the model and to evaluate the elimination of drawbacks. MAPE, MSE, and MAE scores reflect the accuracy and precision capabilities of the model, whereas EVS, and the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e provide reliable insights about the model`s fitness on the dataset. In conclusion, results are presented collectively and refer to the model's reliability and robustness in capturing the dynamic and nonlinear transmission inside the network.\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"666\" height=\"357\"\u003e\u003c/p\u003e\u003cp\u003eConsidering the model`s fitness, exceptionally high EVS scores indicate that more than 99% of the prediction variance inside the network is captured by the model. Similarly, R\u0026sup2; scores confirm the robust accuracy by illustrating that 99% of the predicted residuals are coming from the original values. In addition, MAPE values provide a comparative assessment of the models forecasting performance by expressing the deviations of predictions as a percentage, hence consistently low MAPE scores highlight the strong prediction accuracy of the model for each subset.\u003c/p\u003e \u003cp\u003eFinally, comparing the bootstrapping test results, same upper and lower bound confidence intervals (CI) of each metric on different subsets highlight that the performance score disparities across the subsets are statistically not significant, therefore there is no substantial sign of overfitting and the model is absolutely reliable. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e proposes another perspective for the model`s robust prediction performance by exhibiting the actual and predicted plots of the residuals and indicates that most remarkable deviations are observed for the NASDAQ index, which has the highest net transmissions inside the network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssessment and Validity Scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMSE\u003c/b\u003e:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1042.82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e433.97\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSE CI Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.148563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSE CI Upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.235896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.235896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAPE\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPE CI Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e 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colname=\"c1\"\u003e \u003cp\u003eEVS CI Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVS CI Upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.983561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2 CI Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2 CI Upper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.986528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.986528\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"},{"header":"4. Conclusions","content":"\u003cp\u003eThis research explores the volatility connectedness in a geographically diversified and extensive portfolio of variables, containing the biggest stock and DeFi markets by deploying an original and fine-tuned DNN-TVP-VAR model. Briefly, model is developed by incorporating novel DNN and TVP-VAR structures to eliminate the drawbacks of the parametric and stationary methods through simulating the dependencies and optimizing the model parameters to encapsulate the asymmetric, dynamic, and non-linear transmissions. Model parameters, including the learning rate, batch size, number of epochs, number of lags, and rolling window size are best tuned through original grid search mechanisms, and the model is deployed by using the final results. Last of all, model`s strength and the successful elimination of drawbacks are validated by applying novel bootstrapping tests on the performance score disparities of the model across the subsets.\u003c/p\u003e \u003cp\u003eTotal transmission results identify substantial transmissions from NASDAQ, BTC, NYSE, and SSE to BNB, EURONEXT, TET, and ETH, and illustrate the influence of the important events on the financial contagion, involving Brexit, first and second waves of Covid-19, and the military conflicts between Russia and Ukraine. In particular, total transmission volume sharply declines during the economic downturn caused by Brexit and substantial rises in early 2020 due to the first Covid-19 wave. Subsequently, it reaches a local peak during the second pandemic wave in March 2021, and finally peaks at the beginning of the Ukrainian war in February 2022. Furthermore, pairwise transmission graphs of the stock markets and BTC illustrate similar patterns with the total transmissions graph and explicitly reflect the impacts of the examined global events.\u003c/p\u003e \u003cp\u003eFinally, model assessment indicates that high EVS and R\u0026sup2; scores imply the robust fitness and precision performance, and the exceptionally low MAPE values signify the excellent accuracy of the model. Ultimately, bootstrapping findings confirm the strength of the new approach and the achievement of key parameter optimization.\u003c/p\u003e \u003cp\u003eConsequently, this study offers a robust and original approach to investigate the risk transmissions inside the neoteric financial system through simulating the dependencies and capturing asymmetric, dynamic, and nonlinear risk transmissions across the biggest equity and DeFi markets, while eliminating the drawbacks of the integrated models by key parameter optimization. As a result, findings of this study provide invaluable insights to advance the comprehension of financial risk spillover dynamics across the DeFi and equity markets, in addition its original, reliable, and sturdy methodology is anticipated to be adopted by the future studies in addressing new research questions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah M, Chowdhury MAF, Ullah GW (2025) Asymmetric tail risk dynamics, efficiency and risk spillover among FinTech stocks, cryptocurrencies and traditional assets. Glob Financ J, 101082\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed MF, Sarkodie SA, Leirvik T (2023) Mutual coupling between stock market and cryptocurrencies, Heliyon, Volume 9, Issue 5, 2023, e16179, ISSN 2405\u0026ndash;8440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e16179\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e16179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttarzadeh A, Isayev M, Irani F (2024) Dynamic interconnectedness and portfolio implications among cryptocurrency, gold, energy, and stock markets: A TVP-VAR approach. Sustainable Futures 8:100375\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBibi R, Gulzar S, Shahzad SJH (2025) ESG leaders and crypto currency market: Asymmetric TVP-VAR connectedness and investment approaches. 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J Clean Prod, 145002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Liu J, Xie Q (2024) Quantile frequency connectedness between energy tokens, crypto market, and renewable energy stock markets. Heliyon, 10(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu F, Zhang D, Zhang Z (2019) Connectedness and risk spillovers in China\u0026rsquo;s stock market: A sectoral analysis, Economic Systems, Volume 43, Issues 3\u0026ndash;4, 2019, 100718, ISSN 0939\u0026ndash;3625. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecosys.2019.100718\u003c/span\u003e\u003cspan address=\"10.1016/j.ecosys.2019.100718\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounis I, Gupta H, Du AM, Shah WU, Hanif W (2024) Spillover dynamics in DeFi, G7 banks, and equity markets during global crises: A TVP-VAR analysis. Res Int Bus Finance, 102405\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYousaf I, Cui J, Ali S (2024) Dynamic spillover between green cryptocurrencies and stocks: A portfolio implication. Int Rev Econ Finance 96:103661\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Zhang T (2023) Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach. Finance Res Lett 58:104342\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Computational Finance, DeFi Markets, Equity Markets, Financial Contagion, Risk Transmission","lastPublishedDoi":"10.21203/rs.3.rs-9172803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9172803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction of cryptocurrency markets offered unique confidentiality and hedging benefits to investors, since they have been rapidly integrated into the financial system through forming substantial long- and short-term interdependencies with equity markets. Previous studies present contradicting and ambigious conclusions about the financial contagion between the equity and crypto markets, nonetheless they do not adequately pose light on the impacts of global events on the relationship between these markets. To address these concerns, this research examines the volatility spillovers between the equity and Decentralized Finance (DeFi) markets through simulating the dependencies with the aim of eliminating the bias and drawbacks of the traditional models by appling an optimized and original analytical framework composed of Deep Neural Network (DNN) and Time Varying Parameters Vector Auto Regression (TVP-VAR) models. Results identify substantial transmission from Bitcoin (BTC), Nasdaq 100 (NASDAQ), Shanghai Stock Exchange (SSE), and New York Stock Exchange (NYSE) to BNB, Euronext, Tether (TET), and Ethereum (ETH) and reflect the significant impact of the global important events on the financial spillovers. Model assessment results confirm the robust accuracy, fitness, reliability, and validity of the model, as well as the success of key parameter optimization.\u003c/p\u003e","manuscriptTitle":"The Crypto-Equity Nexus: A Novel Simulation of Risk Transmission Channels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-28 13:19:31","doi":"10.21203/rs.3.rs-9172803/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85649c30-e515-42ff-b798-9572c2c3c089","owner":[],"postedDate":"March 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-28T13:19:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-28 13:19:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9172803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9172803","identity":"rs-9172803","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}