A study of the machine learning approach and the MGARCH-BEKK model in volatility transmission - Prashant Joshi; Jinghua Wang; Michael Busler (Journal of Risk and Financial Management, 2022)
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 GA2M 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: C32; C58; C63; 330; MGARCH-BEKK; GA2M; machine learning; volatility spillovers robustness; cryptocurrency