Climate Risks and Realized Volatility of Major Commodity Currency Exchange Rates

We report that climate-related risks have predictive value useful for forecasting the intraday-data-based realized volatility of exchange-rate returns of eight major fossil fuel-exporters (Australia, Brazil, Canada, Malaysia, Mexico, Norway, Russia, and South Africa). We study a wide array of metrics capturing risks associated with climate change, derived from data directly on variables such as abnormal patterns of temperature, precipitation, number of heating degree days, number of cooling degree days, and wind speed, as well as Google search volume and media coverage on the topic. We also control for various other moments (realized skewness, realized kurtosis, realized good and variance, upside and downside tail risk, and jumps) and estimate our forecasting models using random forests, a machine-learning technique tailored to analyze models with many predictors.