Stock market tumble sparks crypto chaos: A crash risk spillover analysis
Stock market tumble sparks crypto chaos: A crash risk spillover analysis

Summary/Abstract: The study employs an empirical Bayesian estimation approach to examine how the crash risk of the G-7 (United States [US], United Kingdom [UK], Japan, Germany, Canada, and France excluding Italy) and Chinese equity markets affects the crash risk of the top 11 cryptocurrencies. Two crash risk measures were adopted to determine the monthly crash risk of the two types of markets, which are the most appropriate for skewed returns. Four separate models were estimated using the empirical Bayes estimation method because it considers heterogeneity, is more efficient than least squares, and facilitates more accurate coefficient estimation. The results reveal that the German stock market's crash risks are significantly and contemporaneously associ-ated with the crash risk of all 11 crypto-currencies, indicating that the German equity market is not a reliable diversifier for crypto-currencies. The crash risks of the US, UK, and Japanese (German and Canadian) equity markets have a positive (negative) impact on the crash risk of cryptocurrency markets with a one-month lag. Generally, lagged crash risks have a more substantial influence on crypto-currency crash risk, suggesting that historical crashes in equity markets are better predictors of cryptocurrency crashes. The one-month significant delay effect may present arbitrage opportunities because the risk of crashes in stock markets may signal potential crashes in cryptocurrencies one month in advance. A series of robustness checks confirmed the results of the analysis and the validity of our conclusions. These findings suggest that crypto investors and policy-makers should pay attention to historical events in equity markets. Investors and portfolio managers in the cryptocurrency market should monitor unex-pected fluctuations in the stock market, particularly significant declines that could result in significant losses in the future.

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