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The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis

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  • Apostolos Ampountolas

Abstract

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish-Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model's results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets' sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR's highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).

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  • Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis," Papers 2307.09137, arXiv.org.
    • Handle: RePEc:arx:papers:2307.09137
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    1. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Ali, Fahad & Jiang, Yuexiang & Sensoy, Ahmet, 2021. "Downside risk in Dow Jones Islamic equity indices: Precious metals and portfolio diversification before and after the COVID-19 bear market," Research in International Business and Finance, Elsevier, vol. 58(C).
    5. Uddin, Gazi Salah & Yahya, Muhammad & Goswami, Gour Gobinda & Lucey, Brian & Ahmed, Ali, 2022. "Stock market contagion during the COVID-19 pandemic in emerging economies," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 302-309.
    6. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    7. Nguyen, Khanh Quoc, 2022. "The correlation between the stock market and Bitcoin during COVID-19 and other uncertainty periods," Finance Research Letters, Elsevier, vol. 46(PA).
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    10. Ashraf, Badar Nadeem, 2020. "Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    11. Huthaifa Alqaralleh & Alessandra Canepa, 2021. "Evidence of Stock Market Contagion during the COVID-19 Pandemic: A Wavelet-Copula-GARCH Approach," JRFM, MDPI, vol. 14(7), pages 1-18, July.
    12. Caporale, Guglielmo Maria & Plastun, Alex, 2019. "The day of the week effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 31(C).
    13. Lee, Sang-Won & Hansen, Bruce E., 1994. "Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 10(1), pages 29-52, March.
    14. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
    15. Umar, Zaghum & Gubareva, Mariya, 2020. "A time–frequency analysis of the impact of the Covid-19 induced panic on the volatility of currency and cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    16. Urquhart, Andrew & Zhang, Hanxiong, 2019. "Is Bitcoin a hedge or safe haven for currencies? An intraday analysis," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 49-57.
    17. Itay Goldstein & Ralph S J Koijen & Holger M Mueller, 2021. "COVID-19 and Its Impact on Financial Markets and the Real Economy [A model of endogenous risk intolerance and LSAPs: Asset prices and aggregate demand in a “COVID-19” shock]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5135-5148.
    18. Conlon, Thomas & McGee, Richard, 2020. "Safe haven or risky hazard? Bitcoin during the Covid-19 bear market," Finance Research Letters, Elsevier, vol. 35(C).
    19. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    20. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
    21. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    22. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    23. Apostolos Ampountolas, 2022. "Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models," IJFS, MDPI, vol. 10(3), pages 1-22, July.
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    Cited by:

    1. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    2. Mukul Bhatnagar & Sanjay Taneja & Ramona Rupeika-Apoga, 2023. "Demystifying the Effect of the News (Shocks) on Crypto Market Volatility," JRFM, MDPI, vol. 16(2), pages 1-16, February.
    3. Otabek Sattarov & Fazliddin Makhmudov, 2025. "Risk-Aware Crypto Price Prediction Using DQN with Volatility-Adjusted Rewards Across Multi-Period State Representations," Mathematics, MDPI, vol. 13(18), pages 1-29, September.
    4. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    5. Anas Eisa Abdelkreem Mohammed & Henry Mwambi & Bernard Omolo, 2024. "Time-Varying Correlations between JSE.JO Stock Market and Its Partners Using Symmetric and Asymmetric Dynamic Conditional Correlation Models," Stats, MDPI, vol. 7(3), pages 1-16, July.
    6. Mohamed M. Sraieb & Shahnawaz Muhammed & Vladimir Dženopoljac & Samet Gunay, 2025. "Determinants of Russia’s probability of default: evidence from domestic and global indicators," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(3), pages 854-882, September.

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