Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock returns volatility. To this end, we do not only apply the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS model to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock returns volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than classical methods. Our findings have important implications for academics, investors and policymakers.