Analysing the Predictability of Climate Risks on the Conditional Distributions of Bank Returns and Volatility: An International Perspective

As climate risks become more prevalent, it is imperative that banks across the world analyse the potential impacts of these physical and transitional risks, not only on their performance in terms of returns but also on the volatility of their returns, given the significant ripple effects to the financial system. This paper contributes to the limited, existing research by asking how these risks impact the banking sector through the analyses of climate risk effects on the banking sectors' returns as well as volatility, globally, making use of k-th order nonparametric causality-in-quantiles approach based on daily data from 2014 to 2022 for 24 countries. The results indicate that, linear causality test fail to depict evidence of predictability from climate risks to bank stock returns, likely due to misspecification arising out of nonlinearity and structural breaks, the. However, using our robust framework of k-th order nonparametric causality-in-quantiles test, we show strong evidence of predictability for both returns and volatility over their respective entire conditional distributions. Our results have important implications.