Abstract:
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 apply mixed data sampling models such as GARCH-MIDAS and DCC-MIDAS. To deal with volatility clustering, asymmetric effects, and negative skewness in innovations, which characterize our dataset, we use those models in asymmetric form with a bivariate skew-t distribution. Firstly, the GARCH-MIDAS models indicate that climate risk has a significant impact on the long-run volatility of brown energy stocks. Secondly, the DCC-MIDAS models reveal that the long-run correlation of green-brown stock returns decreases with the climate risk, suggesting a negative effect and hedging opportunities. Thirdly, the hedging analysis shows 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. The results are robust to an out-of-sample analysis under various refitting window sizes. They matter to portfolio and risk managers for energy transition and portfolio decarbonization.
