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A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission

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  • Prashant Joshi

    (School of Business, Saint Martin’s University, 5000 Abbey Way SE, Lacey, WA 98503, USA)

  • Jinghua Wang

    (Martin Tuchman School of Management, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA)

  • Michael Busler

    (School of Business, Stockton University, 101 Vera King Farris Drive, Galloway, NJ 08205, USA)

Abstract

This study analyzes the volatility spillover effects in the US stock market (S&P500) and cryptocurrency market (BGCI) using intraday data during the COVID-19 pandemic. As the potential drivers of portfolio diversification, we measure the asymmetric volatility transmission on both markets. We apply MGARCH-BEKK and the algorithm-based G A 2 M machine learning model. The negative shocks to returns impact the S&P500 and the cryptocurrency market more than the positive shocks on both markets. This study also indicates evidence of unidirectional cross-market asymmetric volatility transmission from the cryptocurrency market to the S&P500 during the COVID-19 pandemic. The research findings show the potential benefit of portfolio diversification between the S&P500 and BGCI.

Suggested Citation

  • Prashant Joshi & Jinghua Wang & Michael Busler, 2022. "A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission," JRFM, MDPI, vol. 15(3), pages 1-9, March.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:3:p:116-:d:762194
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    References listed on IDEAS

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    Cited by:

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    2. Hsu, Shu-Han & Cheng, Po-Keng & Yang, Yiwen, 2024. "Diversification, hedging, and safe-haven characteristics of cryptocurrencies: A structural change approach," International Review of Financial Analysis, Elsevier, vol. 93(C).

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