Dynamic Cross‐Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets

Abstract

This paper examines the spillover effect between bitcoin, gold, crude oil, and major stock markets by using the MSV model with dynamic correlation and Granger causality. The empirical results of the DC-GC-MSV model are logically correct and convergent. The DIC test result has proved that the DC-GC-MSV model is better and more accurate. Bitcoin has no significant Granger causality spillover effect than other assets. As a safe haven product for stock assets, gold price has one-way spillover effect from stock market volatility. Moreover, crude oil has the highest correlation with the stock market. In the recent COVID-19 epidemic and the sluggish economic environment, investors need to consider a balanced asset allocation among low-correlation assets, medium-correlation assets, and high-correlation assets to reduce risks.

Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin

“…This finding is consistent with past studies (Symitsi and Chalvatzis, 2018; Shahzad et al. , 2019, Zhang and He, 2021; Jin et al. , 2019).…”

“…Zeng et al. (2020) and Zhang and He (2021) found similar results while exploring the co-movement between gold and Bitcoin markets.…”

“…For example, Su and Li (2020), Gkillas et al. (2020) and Zhang and He (2021) show a bi-directional return and volatility spillover among S&P 500, OIL, GOLD and Bitcoin. Antonakakis et al.…”

“…Besides, they also unveil that the safe-haven roles of Bitcoin, gold and commodities are time-varying and differ across the stock markets. Zhang and He (2021) investigate the spillover effects of Bitcoin, gold, crude oil and major stock market indices, and find that Bitcoin has no significant Granger causality spillover effects compared to other assets. Also, the price of gold, as a safe haven product for stock assets, has a one-way spillover effect from stock market volatility.…”

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

“…While this paper focuses on three different but integrated methods, there are also many studies that use different techniques to show that the volatility spillovers in the cryptocurrency markets are still prevalent. These methods can be classified as follows: rolling sample analysis (Fasanya et al 2021 ), BEKK-MGARCH analysis (Katsiampa et al 2019b ), machine learning techniques (e.g., linear models, random forests, and support vector machines) (Sebastião and Godinho 2021 ), an integrated cluster detection, optimization, and interpretation approach (Li et al 2021 ), Markov regime-switching vector autoregressive with exogenous variables (MS-VARX) model (Shahzad et al 2021 ), generalized VAR framework (Melki 2020 ), bankruptcy prediction model (Kou et al 2021a , b ), a hybrid interval type-2 fuzzy multidimensional decision-making approach (Kou et al 2021a , b ), and multivariate stochastic volatility model (Zhang and He 2021 ). The common point of using these methods is to capture the inherent secular and cyclical movements in digital asset markets.…”