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

Prashant Joshi, Jinghua Wang and Michael Busler
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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

JRFM, 2022, vol. 15, issue 3, 1-9

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.

Keywords: MGARCH-BEKK; GA 2 M; machine learning; volatility spillovers robustness; cryptocurrency (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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