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Climate Risks And Predictability Of Commodity Returns And Volatility: Evidence From Over 750 Years Of Data

Author

Listed:
  • JACOBUS NEL

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

  • RANGAN GUPTA

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

  • MARK E. WOHAR

    (��College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA)

  • CHRISTIAN PIERDZIOCH

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

Abstract

We analyze whether metrics of climate risks, as captured primarily by changes in temperature anomaly and its stochastic volatility (SV), can predict returns and volatility of 25 commodities, covering the overall historical period of 1258 to 2021. To this end, we apply a higher-order nonparametric causality-in-quantiles test to not only uncover potential in-sample predictability in the entire conditional distribution of commodity returns and volatility but also to account for nonlinearity and structural breaks which exist between commodity returns and the metrics of climate risks. We find that, unlike in the misspecified linear Granger causality tests, climate risks do predict commodity returns and volatility, though the impact on the latter is stronger, in terms of the coverage of the conditional distribution. Insights from our findings can benefit academics, investors, and policymakers in their decision-making.

Suggested Citation

  • Jacobus Nel & Rangan Gupta & Mark E. Wohar & Christian Pierdzioch, 2024. "Climate Risks And Predictability Of Commodity Returns And Volatility: Evidence From Over 750 Years Of Data," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-40, November.
    • Handle: RePEc:wsi:ccexxx:v:15:y:2024:i:04:n:s2010007824500039
    DOI: 10.1142/S2010007824500039
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    More about this item

    Keywords

    Climate risks; commodities; returns and volatility predictions; higher-order nonparametric causality-in-quantiles test;
    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
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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