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 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.
Highlights
Journal of Risk and FinancialThe cryptocurrency market reshaped the traditional concepts of the financial markets.Blockchain technology using computer science knowledge created a financial revolution, impacting the financial markets from every perspective
The study explores the application of the new technology—generalized additive 2 model (GA2 M) in finance beyond the classical time series approaches
The MGARCH-BEKK model found a lack of volatility spillover between the markets
Summary
The cryptocurrency market reshaped the traditional concepts of the financial markets. Our research is the first study that combines the machine learning approach and the MGARCH-BEKK model to study the strength and linkage of volatility transmission between the cryptocurrency and US stock markets. Our research question is about whether there are intraday volatility interactions between the crypto and the stock markets To answer this question, we employ MGARCHBEKK (Engle and Kroner 1995) and a machine learning GA2 M framework to investigate volatility transmission between the BGCI and S&P500. This paper tries to further the cryptocurrency literature in numerous ways It uses a multivariate asymmetric GARCH model to examine the intraday asymmetric volatility spillover. It studies the most recent period, during the COVID-19 pandemic.
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