Abstract:

Cryptocurrency market is an independent emerging financial market whose intrinsic value is difficult to accurately estimate. Cryptocurrency prices are largely influenced by investor sentiment and will fluctuate wildly when major emergencies occur. In the context of financial market integration and investor homogeneity， the risk contagion effect between the cryptocurrency market and traditional financial markets is significant. The cross-contagion characteristics of tail risks between cryptocurrencies and the global stock market under different market conditions and time periods are different. Besides， it needs to be studied from both the time domain and frequency domain perspectives.By combining the quantile vector autoregression （QVAR） model with the frequency domain perspective， the quantile time-frequency risk spillover model can be used to study this issue. First， the EGARCH model using leptokurtosis and thick tail distribution is used to fit the fluctuations of cryptocurrency and global stock markets. On this basis， wavelet coherence is used to examine the relationship between cryptocurrency and the stock market of major economies in different time and frequency domains. Finally， the tail risk spillover network between cryptocurrencies and global stock markets is constructed. By analyzing the changes in tail risk spillover effects based on different quantiles in the time-frequency domain， the tail risk contagion characteristics of cryptocurrencies and global stock markets are studied. All sample data comes from the Investing.com.It is found that the spillover index based on the quantile measure can better capture the tail risk spillover effects of cryptocurrencies and global stock markets under different impact sizes， while the traditional spillover index based on the conditional mean measure will underestimate the true tail risk spillover level. Under extreme conditions， the tail risk spillover effect between cryptocurrencies and global stock markets is significantly stronger than in normal conditions， and is obviously asymmetric. From the frequency domain perspective， the tail risk spillover between cryptocurrencies and global stock markets shows significant cyclical characteristics. In addition， cryptocurrencies are net risk bearers under normal conditions， but they have obvious net risk spillover effects when the market is subject to positive shocks.Different from the previous research， the innovative contributions of this article are mainly reflected in the following aspects： （1） After characterizing the leptokurtosis and thick tail characteristics of the return distribution of cryptocurrencies and global stock markets， wavelet analysis method is used to examine the dynamic changes in the fluctuation linkage between cryptocurrencies and the stock markets of major economies in the time domain and frequency domain； （2） Based on the dual dimensions of time domain and frequency domain， tail risk spillover networks between cryptocurrency and global stock markets are built， and the characteristics of tail risk spillover effects in different time domains and frequency domains are deeply analyzed； （3） The tail risk spillover index under the conditional mean is expanded through the quantile vector autoregressive model， and the tail risk spillover effects of cryptocurrencies and global stock markets under different shock scales are examined， revealing the asymmetry of the tail risk spillover effects between cryptocurrencies and global stock markets in extreme rising and falling states. By examining the cross-contagion characteristics of tail risks between cryptocurrencies and global stock markets under different market conditions and time periods， it will help regulatory authorities formulate， implement and improve cryptocurrency regulatory policies.

Key words: cryptocurrency, tail risk spillover, conditional quantile, frequency domain