Return Volatility, Correlation, and Hedging of Green and Brown Stocks: Is there a Role for Climate Risk Factors?

We examine the effects of three monthly climate risk factors, climate policy uncertainty (CPU), climate change news (CCN), and negative climate change news (NCCN) on the long-run volatilities and correlation of daily green and brown energy stock returns, and perform a hedging analysis. Given that our dataset combines daily and monthly data, we rely on mixed data sampling models such as GARCH-MIDAS and DCC-MIDAS in standard and asymmetric forms with a bivariate skew-t distribution, which also allows us to deal with volatility clustering, asymmetric effects, and negative skewness in innovation which characterize our dataset. Firstly, the results of the GARCH-MIDAS models show evidence that climate risk contains information useful to improve the prediction of return volatility of brown energy stocks. Secondly, the results of the DCCMIDAS model indicate that climate risk reduces the green-brown returns correlation, suggesting a negative effect and hedging opportunities. Thirdly, the results of the hedging analysis show that incorporating a climate risk factor, especially NCCN, into the long-run component of dynamic correlation significantly improves the hedging performance between green and brown energy stock indices, and this are robust to an out-of-sample analysis under various refitting window sizes. These results matter to portfolio and risk managers for energy transition and portfolio decarbonization.