Tail risk interconnectedness between cryptocurrency and clean energy markets under geopolitical conflicts

Currently, geopolitical conflicts occur frequently around the world, and the volatility of the cryptocurrency market and the international energy market, which are important components of the financial system, has increased significantly. Against this background, this paper characterizes the intricate interconnectedness of tail risk between cryptocurrencies and global energy markets under geopolitical conflict influence. We first use wavelet coherence approach to explore the market time-frequency correlation, after which the stochastic volatility model with leptokurtic11The leptokurtic indicates that the probability density function is steeper near the mean, indicating that the yield is more densely distributed in the neighborhood of the mean, reflecting the concentration of normal market fluctuations. Its characteristics are determined by kurtosis> 3. and fat-tailed22The fat-tailed distribution means that the tail decay rate of the returns distribution is lower than that of the normal distribution, resulting in the probability of extreme gains or losses being significantly higher than the theoretical value under the normal assumption, reflecting the frequency of "black swan" events in the financial market. Its characteristics are determined by the tail index≠0. distribution (GJR-GARCH-SGED) and the quantile vector autoregression (QVAR) model are adopted to systematically investigate the characteristics of tail risk spillover among geopolitical risks, Bitcoin and clean energy markets, considering the viewpoints of the time and frequency domains. The tail risk spillover network exhibits cyclical characteristics and market heterogeneity, according to research findings. Extreme circumstances greatly elevate the risk spillover effects of geopolitical risks, cryptocurrency and energy markets compared to normal circumstances, and the tail risk spillovers under extreme circumstances are asymmetric. When market is in normal and extreme decline condition, long-term factors dominate the total spillover effect, according to the frequency domain perspective, while contagion of short-term risk is especially significant in extreme rise state. Moreover, geopolitical risks and cryptocurrencies are net risk recipients under normal conditions, but they show obvious positive tail risk spillovers at higher quantiles. The research conclusions provide theoretical basis for understanding the cross-market risk contagion mechanism under extreme shocks.