We offer global evidence on the macroeconomic effects of climate change for a panel of 22 countries classified by economic groupings. We show that: globally, climate change is inflationary; the negative impacts of climate change on the exchange rate markets is only evident in emerging economies; stock markets could hedge natural climate risks but fail to hedge climate risks due to policy uncertainty.

In this study, we estimate a multi-country Threshold-Augmented Global Vector Autoregressive (TGVAR) model to analyze the response of real GDP of emerging economies (Brazil, India, China, and South Africa) with reference to selected advanced economies (US, UK, & Germany) to the COVID-19 shock. The result of the counterfactual analysis beyond the 2019Q4 indicates that the impact of COVID-19 shock on real GDP is pervasive and more prevalent in the developed than the emerging economies. Our model forecasts real GDP growth of emerging countries more precisely, but we attribute the shortfalls in the projections for advanced economies to the efficacy of fiscal and unconventional monetary policies to speed up the recovery in these countries.

This study contributes to the emerging literature offering alternative measures of uncertainty due to the COVID-19 pandemic. We combine both news-and macro-based trends to construct an index. The former involves the use of Google trends with plausible variants of words used to capture the pandemic, which are combined using principal components analysis to develop a news-based index. For the macro-based index, we identify global factors such as oil price, stock price, Dollar index, commodity index and gold price, and thereafter we obtain the macro-based uncertainty using variants of stochastic volatility models estimated with Bayesian techniques and using a dynamic factor model. Consequently, the new (composite) index is constructed by combining the news- and macro-based indexes using principal components analysis. Our empirical applications of the index to the stock return predictability of the countries hit worst by the pandemic confirm the superiority of the composite index over the existing news-based index in both the in-sample and out-of-sample forecast horizons. Our results are also robust to forecast horizons and competing model choices.

This study investigates the effect of climate change on Asian stock markets using Generalized Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling (GARCH-MIDAS) model. The results reveal that long-term stock return volatility of about 40% of Asian stock markets are unaffected by climate change. This implies that about 40% of investments in the Asian stock markets are insensitive to climate change financing. More awareness about investment in climate-oriented stocks is recommended.