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Climate Risks and U.S. Stock-Market Tail Risks: A Forecasting Experiment Using over a Century of Data

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Renee van Eyden

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

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.

Suggested Citation

  • Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & Renee van Eyden, 2021. "Climate Risks and U.S. Stock-Market Tail Risks: A Forecasting Experiment Using over a Century of Data," Working Papers 202165, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202165
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    2. Massimiliano Caporin & Petre Caraiani & Oguzhan Cepni & Rangan Gupta, 2024. "Predicting the Conditional Distribution of US Stock Market Systemic Stress: The Role of Climate Risks," Working Papers 202407, University of Pretoria, Department of Economics.
    3. Polat, Onur & Gupta, Rangan & Cepni, Oguzhan & Ji, Qiang, 2024. "Can municipal bonds hedge US state-level climate risks?," Finance Research Letters, Elsevier, vol. 67(PB).
    4. Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024. "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    5. Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2024. "Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks," Working Papers 202424, University of Pretoria, Department of Economics.
    6. Kejin Wu & Sayar Karmakar & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa," Working Papers 202326, University of Pretoria, Department of Economics.
    7. Matteo Foglia & Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2024. "Long-Span Multi-Layer Spillovers between Moments of Advanced Equity Markets: The Role of Climate Risks," Working Papers 202415, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    Stock market; Tail risks; Climate risks; Forecasting; Asymmetric loss;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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