Quantile return and volatility spillovers and drivers among energy, electricity, and cryptocurrency markets

Abstract

Over the past decade, the cryptocurrency market has experienced significant growth. However, the dynamics of risk spillover between various types of cryptocurrencies and the electricity market, as well as energy markets, under different quantile conditions remain ambiguous. To address this gap, this paper utilizes the Quantile Vector Autoregression (QVAR) model to examine the returns and volatility spillovers among energy (fossil and clean energy), the electricity market, and cryptocurrencies (clean and dirty cryptocurrency) markets across varying quantile conditions. Additionally, this paper investigates the determinants of spillover effects among these markets. The findings reveal that moderate spillover effects exist among these markets under conditional mean and median quantiles, while such effects are intensified in extreme quantile conditions. Moreover, oil, clean cryptocurrency, wind energy, and geothermal energy typically act as recipients of spillover effects, whereas natural gas, dirty cryptocurrency, bioenergy, solar energy, and, fuel cells generally function as transmitters of spillover effects. The electricity market serves as a recipient under mean and median quantile conditions but acts as a transmitter under extreme conditions. Furthermore, EPU, CFGI, TERM, and COVID-19 significantly enhance spillover effects among these three markets. These insights offer valuable implications for investors and policymakers.