Climate risks and U.S. stock-market tail risks : a forecasting experiment using over a century of data
Loading...
Date
Authors
Salisu, Afees A.
Pierdzioch, Christian
Gupta, Rangan
Van Eyden, Renee
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Abstract
We examine the predictive value of the uncertainty associated with growth in temperature for stock-market tail risk in the United States using monthly data that cover the sample period from 1895:02 to 2021:08. To this end, we measure stock-market tail risk by means of the popular Conditional Autoregressive Value at Risk (CAViaR) model. Our results show that accounting for the predictive value of the uncertainty associated with growth in temperature, as measured either by means of standard generalized autoregressive conditional heteroskedasticity (GARCH) models or a stochastic-volatility (SV) model, mainly is beneficial for a forecaster who suffers a sufficiently higher loss from an underestimation of tail risk than from a comparable overestimation.
Description
Keywords
Asymmetric loss, Climate risks, Forecasting, Stock market, Tail risks
Sustainable Development Goals
Citation
Salisu, A. A., Pierdzioch, C., Gupta, R., & van Eyden, R. (2023). Climate risks and U.S. stock-market tail risks: A forecasting experiment using over a century of data. International Review of Finance, 23(2), 228–244. https://doi.org/10.1111/irfi.12397.