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The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models

Author

Listed:
  • Virginie Terraza

    (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)

  • Aslı Boru İpek
  • Mohammad Mahdi Rounaghi

Abstract

The spread of the coronavirus has reduced the value of stock indexes, depressed energy and metals commodities prices including oil, and caused instability in financial markets around the world. Due to this situation, investors should consider investing in more secure assets, such as real estate property, cash, gold, and crypto assets. In recent years, among secure assets, cryptoassets are gaining more attention than traditional investments. This study compares the Bitcoin market, the gold market, and American stock indexes (S&P500, Nasdaq, and Dow Jones) before and during the COVID-19 pandemic. For this purpose, the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets. Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin, gold, and stock markets. In particular, we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period. This paper provides practical impacts on risk management and portfolio diversification.

Suggested Citation

  • Virginie Terraza & Aslı Boru İpek & Mohammad Mahdi Rounaghi, 2024. "The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models," Post-Print hal-04395168, HAL.
    • Handle: RePEc:hal:journl:hal-04395168
    DOI: 10.1186/s40854-023-00520-3
    Note: View the original document on HAL open archive server: https://hal.umontpellier.fr/hal-04395168v1
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    References listed on IDEAS

    as
    1. Imran Yousaf & Shoaib Ali, 2020. "Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    2. Muhammad Ali Nasir & Toan Luu Duc Huynh & Sang Phu Nguyen & Duy Duong, 2019. "Forecasting cryptocurrency returns and volume using search engines," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-13, December.
    3. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
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    Citations

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    Cited by:

    1. Dimitriadis, Konstantinos A. & Koursaros, Demetris & Savva, Christos S., 2025. "Exploring the dynamic nexus of traditional and digital assets in inflationary times: The role of safe havens, tech stocks, and cryptocurrencies," Economic Modelling, Elsevier, vol. 151(C).
    2. Hossein Tarighi & Grzegorz Zimon & Mohammad Javad Sheikh & Mohammad Sayrani, 2024. "The Impact of Firm Risk and the COVID-19 Crisis on Working Capital Management Strategies: Evidence from a Market Affected by Economic Uncertainty," Risks, MDPI, vol. 12(4), pages 1-33, April.
    3. Liu, Peng & Yuan, Ying, 2024. "Is Bitcoin a hedge or safe-haven asset during the period of turmoil? Evidence from the currency, bond and stock markets," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    4. Wu, Ran, 2025. "Forecasting the European Union allowance price tail risk with the integrated deep belief and mixture density networks," Chaos, Solitons & Fractals, Elsevier, vol. 199(P2).
    5. Zeinedini, Shabnam & Karimi, Mohammad Sharif & Khanzadi, Azad & Falahati, Ali, 2024. "Impact of oil and gold prices on Bitcoin price during Russia-Ukraine and Israel-Gaza wars," Resources Policy, Elsevier, vol. 99(C).
    6. Kamphol Panyagometh, 2024. "The effect of COVID-19 and U.S. monetary policy on Bitcoin and stock market volatility: an application of DCC-GARCH model," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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