Dynamic correlations and volatility spillovers between subsectoral clean-energy stocks and commodity futures markets: A hedging perspective

Abstract

This study investigates the time-varying connectedness between subsectoral clean-energy stocks and fossil fuel energy commodities (crude oil, natural gas, and coal) over the period of December 2013-January 2023 employing the Diebold and Yilmaz approach and the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model. According to the findings, oil transmits the highest volatility spillover shocks to biofuels, and the least to the fuel cell industry. Both natural gas and coal transmit the highest volatility spillover shocks to energy storage, and the least to geothermal and green information technology, respectively. The study also finds strong and time-varying volatility connectedness among clean-energy assets and fossil fuels, significantly affected by global extreme events, such as the COVID-19 pandemic and the Russia-Ukraine conflict. Additionally, the study provides time-varying and mean optimal hedge ratios with optimal portfolio weights for investors. The empirical results are robust, and important portfolio and policy implications based on empirical findings are provided.