"A Study of the Machine Learning Approach and the MGARCH-BEKK Model in " by Prashant Joshi, Jinghua Wang et al.
 

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

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