Investigating the effect of climate uncertainty on global commodity markets

This study aims to investigate the effect of climate uncertainty on global commodity markets. To do so, I modify Mumtaz and Theodoridis's (2018) time-varying factor-augmented VAR (FAVAR) with stochastic volatility in mean model. By incorporating the information from a large data set with an efficiently constructed dynamic factor structure, I not only overcome the omitted variable problem but also maintain the efficiency of the estimator to identify the nonlinear climate effects. Moreover, I apply Chang et al.'s (2017) endogenous regime switching in mean model for the climate variable, to overcome the statistical problem generated by the periodicity. The main empirical results can be summarized as follows. First, climate uncertainty generates an inflationary pressure on agricultural food, non-energy, and energy commodities for the El Niño years. Second, individual items such as maize and soybeans are more sensitive than the aggregated commodity indices to the effect of climate uncertainty. Third, climate uncertainty generates a negative supply shock, whereas market uncertainty generates a negative demand shock on the individual agricultural items.