The Climate Factor in Volatility Forecasting: Analyzing the Impact of Climate Variables on VIX.

Climate change is emerging as a critical factor influencing economic activity and financial markets. While existing literature has covered the impact of macroeconomic variables on market volatility, the role of climate risk remains underexplored. This thesis investigates whether including climate variables into forecasting models enhances the predictability of CBOE Volatility Index (VIX), studying also the potential role of climate factors in forecasting S&P 500 returns. The study employs an augmented autoregressive (AR) framework, integrating macro-financial and climate-related factors derived from two large datasets: the FRED-MD database for economic indicators and the PRISM climate dataset for high-resolution climate observations for USA. Empirical results suggest that climate variables improve forecasting accuracy of VIX, while they do not have an impact on S&P 500 returns, suggesting that climate risk may be transmitted to financial markets primarily through volatility rather than direct stock price movements. These findings highlight the growing importance of climate considerations in financial risk management and show the need for integrating climate risks into volatility forecasting frameworks.