A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission
Document Type
Article
Publication Date
3-1-2022
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 GA2 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.
Identifier
85130503580 (Scopus)
Publication Title
Journal of Risk and Financial Management
External Full Text Location
https://doi.org/10.3390/jrfm15030116
e-ISSN
19118074
Issue
3
Volume
15
Recommended Citation
Joshi, Prashant; Wang, Jinghua; and Busler, Michael, "A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission" (2022). Faculty Publications. 3073.
https://digitalcommons.njit.edu/fac_pubs/3073