Statistical Methods in Finance 2025

Predicting the Conditional Distributions of Inflation and Inflation Uncertainty in South Africa: The Role of Climate Risks

By:Rangan Gupta
University of Pretoria

This paper analyzes the predictive effect of climate risks on inflation and inflation uncertainty (volatility) of an emerging market inflation targeting economy: South Africa, using a multivariate nonparametric higher-order causality-in-quantiles test. In this regard, we obtain a Google Trends search-based Climate Attention Index for South Africa (CAI-SA), involving both local and global terms dealing with physical and transition risks over the monthly period of January 2004 to September 2024. Using the CAI-SA, we find that, even though linear Granger causality tests fail to show any evidence of prediction of overall and food and non-alcoholic beverages inflation rates, due to model misspecifications from nonlinearity and structural breaks, the robust multivariate nonparametric framework depicts statistically significant predictability over the entire conditional distribution of not only the two inflation rates, but also their respective volatilities, i.e., squared values. The strongest predictive impact is observed at the tails of the conditional distributions of the first- and second-moment of the two inflation rates. Our findings, in general, are robust to alternative definitions of inflation volatility, exclusion of the control variables, different methods of construction of the CAI, and a bootstrapped version of the test to account for size distortion and low power. Analyses involving signs of the causal impact reveal significant positive association between the CAI-SA and the inflation rates and their volatilities, thus having serious implications for monetary policy decisions in South Africa in the wake of heightened climate risks.