Multi-scale dynamic correlation and information spillover effects between climate risks and digital cryptocurrencies: Based on wavelet analysis and time-frequency domain QVAR

Abstract

This study investigates the multi-scale dynamic correlations and information spillover effects between climate risks and digital cryptocurrencies using wavelet analysis and the Time-frequency Domain QVAR model. By analyzing non-stationary financial time-series data, we uncover latent patterns and quantify the dynamic interactions between climate risks and cryptocurrency markets across different time scales. The findings reveal significant spillover effects, highlighting how climate risks, particularly through energy-intensive mining and extreme weather disruptions, influence cryptocurrency volatility. The research contributes to the understanding of risk transmission mechanisms in emerging financial markets, offering insights into the broader implications of climate risks on global financial stability. The results underscore the importance of integrating climate risk assessments into cryptocurrency market analyses, providing a foundation for informed policy-making and risk management strategies.