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

Prashant Joshi, Jinghua Wang, Michael Busler

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number116
JournalJournal of Risk and Financial Management
Volume15
Issue number3
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

  • Accounting
  • Business, Management and Accounting (miscellaneous)
  • Finance
  • Economics and Econometrics

Keywords

  • cryptocurrency
  • GA M
  • machine learning
  • MGARCH-BEKK
  • volatility spillovers robustness

Fingerprint

Dive into the research topics of 'A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission'. Together they form a unique fingerprint.

Cite this