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

Prashant Joshi; Jinghua Wang; Michael Busler

doi:10.3390/jrfm15030116

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

Prashant Joshi, Jinghua Wang, Michael Busler

MT School of Management

Research output: Contribution to journal › Article › peer-review

4 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 language	English (US)
Article number	116
Journal	Journal of Risk and Financial Management
Volume	15
Issue number	3
DOIs	https://doi.org/10.3390/jrfm15030116
State	Published - Mar 2022

All Science Journal Classification (ASJC) codes

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

Keywords

GA M
MGARCH-BEKK
cryptocurrency
machine learning
volatility spillovers robustness

Access to Document

10.3390/jrfm15030116

Cite this

@article{78c7925e49904b589ef17b50c331de7d,

title = "A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission",

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.",

keywords = "GA M, MGARCH-BEKK, cryptocurrency, machine learning, volatility spillovers robustness",

author = "Prashant Joshi and Jinghua Wang and Michael Busler",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = mar,

doi = "10.3390/jrfm15030116",

language = "English (US)",

volume = "15",

journal = "Journal of Risk and Financial Management",

issn = "1911-8066",

publisher = "MDPI AG",

number = "3",

}

TY - JOUR

T1 - A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission

AU - Joshi, Prashant

AU - Wang, Jinghua

AU - Busler, Michael

PY - 2022/3

Y1 - 2022/3

N2 - 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.

AB - 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.

KW - GA M

KW - MGARCH-BEKK

KW - cryptocurrency

KW - machine learning

KW - volatility spillovers robustness

UR - http://www.scopus.com/inward/record.url?scp=85130503580&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85130503580&partnerID=8YFLogxK

U2 - 10.3390/jrfm15030116

DO - 10.3390/jrfm15030116

M3 - Article

AN - SCOPUS:85130503580

SN - 1911-8066

VL - 15

JO - Journal of Risk and Financial Management

JF - Journal of Risk and Financial Management

IS - 3

M1 - 116

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this