Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century

Abstract

In this study, we contribute to the rapidly growing climate-finance literature by shedding light on the question of whether climate risks have predictive value for stock market returns. We measure climate risks in terms of both the change in the northern hemisphere temperature anomaly and its volatility and the change in the global temperature anomaly and its volatility. We study monthly data for eight advanced countries (Canada, France, Germany, Italy, Japan, Switzerland, the United Kingdom (UK), and the United States (US)). Our sample period runs from 1916 to 2021. We control for cross-market spillovers of stock market returns and volatility as well as other risks including oil-price returns and volatility, geopolitical risks, and the gold-to-silver price ratio as a measure of investor risk aversion. Given this large array of control variables, we apply the Lasso estimator to trace out the incremental predictive value of climate risks for subsequent stock market returns. We find that climate risks do not have systematic predictive value for subsequent stock market returns. We then extend our analysis in two ways. First, we show that climate risks have short-term out-of-sample predictive value for the connectedness of stock market returns. Second, we show that climate risks have predictive power for stock market returns when we study monthly historical UK data for the sample period from 1772 to 2021.

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

“…However, researchers thus far have not incorporated the role of changes in the temperature anomaly and its volatility capturing climate-based rare disaster risks in forecasting stock return volatility in South Africa spanning over a century of historical equity market data. In fact, in earlier studies on climate risks and stock markets, researchers primarily have concentrated on developed countries and on in-sample movements of the first moment [13,15,27,29,30], with the only exception being [14], who have analyzed stock market volatility of the state-level data in the United States (US). When it comes to volatility, the literature thus far has concentrated on predicting second moments of commodity returns due to climate risks (for example, [58][59][60][61][62][63]).…”

“…Our objective is to forecast stock return volatility of an important emerging market economy, namely South Africa, using the informational content of rare disaster risk, over the monthly period from February 1910 to February 2023. In line with the burgeoning literature on climate finance, we use changes in the temperature anomaly and its volatility as an empirical proxy of the theoretical concept of rare disaster risk, as in the advanced financial market movements-based research by [11][12][13][14][15], given that climate change poses a large aggregate risk to the overall macroeconomy and the global financial system due to the associated occurrences of rare disasters originating from physical risks involved with global warming and climate change [16][17][18]. To this end, we study data that span more than a century because climate change is a slow-moving process and its effects have tended to aggravate over time as economies have become more and more industrialized.…”