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

Author

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  • Mehmet Balcilar

    (Department of Economics, Eastern Mediterranean University, Turkish Republic of Northern Cyprus, Via Mersin 10, Famagusta 99628, Turkey; Department of Economics, OSTIM Technical University, Ankara 06374, Turkey)

  • David Gabauer

    (Data Analysis Systems, Software Competence Center Hagenberg, Hagenberg, Austria)

  • Rangan Gupta

    (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)

Abstract

Using monthly data for the eight advanced countries (Canada, France, Germany, Italy, Japan, Switzerland, the United Kingdom (UK), and the United States (US)) over the period from 1916 to 2021, we study 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 the change in the global temperature anomaly and its volatility. In our forecasting models, 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 contribution of climate risks to the predictive performance of our forecasting models. We find that climate risks do not have systematic predictive value for subsequent stock-market returns. Climate risks, however, have short-term out-of-sample predictive value for the connectedness of stock-market returns. Moreover, climate risks have predictive power for stock-market returns when we study historical UK data.

Suggested Citation

  • Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and Forecasting Stock-Market Returns in Advanced Economies Over a Century," Working Papers 202183, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202183
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    as
    1. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    2. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    3. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    4. Christou, Christina & Gupta, Rangan & Jawadi, Fredj, 2021. "Does inequality help in forecasting equity premium in a panel of G7 countries?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    5. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression," International Review of Financial Analysis, Elsevier, vol. 65(C).
    6. Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
    7. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    8. Ströbel, Johannes & Wurgler, Jeffrey, 2021. "What do you think about climate finance?," CEPR Discussion Papers 16622, C.E.P.R. Discussion Papers.
    9. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    10. Balcilar, Mehmet & Roubaud, David & Usman, Ojonugwa & Wohar, Mark E., 2021. "Moving out of the linear rut: A period-specific and regime-dependent exchange rate and oil price pass-through in the BRICS countries," Energy Economics, Elsevier, vol. 98(C).
    11. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan & Plakandaras, Vasilios, 2018. "Dynamic connectedness of uncertainty across developed economies: A time-varying approach," Economics Letters, Elsevier, vol. 166(C), pages 63-75.
    12. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2017. "International stock return predictability: Is the role of U.S. time-varying?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 121-146, February.
    13. Hui Guo & Robert F. Whitelaw, 2006. "Uncovering the Risk–Return Relation in the Stock Market," Journal of Finance, American Finance Association, vol. 61(3), pages 1433-1463, June.
    14. Steven J. Jordan & Andrew Vivian & Mark E. Wohar, 2018. "Stock returns forecasting with metals: sentiment vs. fundamentals," The European Journal of Finance, Taylor & Francis Journals, vol. 24(6), pages 458-477, April.
    15. Darwin Choi & Zhenyu Gao & Wenxi Jiang, 2020. "Attention to Global Warming," The Review of Financial Studies, Society for Financial Studies, vol. 33(3), pages 1112-1145.
    16. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    17. Gupta, Rangan & Mwamba, John W. Muteba & Wohar, Mark E., 2018. "The role of partisan conflict in forecasting the U.S. equity premium: A nonparametric approach," Finance Research Letters, Elsevier, vol. 25(C), pages 131-136.
    18. Rangan Gupta & Anandamayee Majumdar & Mark E. Wohar, 2017. "The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach," Open Economies Review, Springer, vol. 28(1), pages 47-59, February.
    19. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    20. Michael Donadelli & Marcus Jüppner & Antonio Paradiso & Christian Schlag, 2021. "Computing Macro-Effects and Welfare Costs of Temperature Volatility: A Structural Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 347-394, August.
    21. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    22. Mehmet Balcilar & David Roubaud & Ojonugwa Usman & Mark E. Wohar, 2021. "Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?," The World Economy, Wiley Blackwell, vol. 44(1), pages 188-233, January.
    23. Piergiorgio Alessandri & Haroon Mumtaz, 2021. "The Macroeconomic Cost of Climate Volatility," Working Papers 928, Queen Mary University of London, School of Economics and Finance.
    24. Rietz, Thomas A., 1988. "The equity risk premium a solution," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 117-131, July.
    25. Nicolás Magner & Jaime F Lavin & Mauricio Valle & Nicolás Hardy, 2021. "The predictive power of stock market’s expectations volatility: A financial synchronization phenomenon," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    26. Afees A. Salisu & Rangan Gupta, 2022. "Commodity Prices and Forecastability of International Stock Returns over a Century: Sentiments versus Fundamentals with Focus on South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(9), pages 2620-2636, July.
    27. John Y. Campbell, 2007. "Estimating the Equity Premium," NBER Working Papers 13423, National Bureau of Economic Research, Inc.
    28. Stavros Degiannakis & George Filis & Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, , vol. 39(5), pages 85-130, September.
    29. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    30. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    31. Robert J. Barro, 2009. "Rare Disasters, Asset Prices, and Welfare Costs," American Economic Review, American Economic Association, vol. 99(1), pages 243-264, March.
    32. Florian Huber & Tamás Krisztin & Philipp Piribauer, 2017. "Forecasting Global Equity Indices Using Large Bayesian Vars," Bulletin of Economic Research, Wiley Blackwell, vol. 69(3), pages 288-308, July.
    33. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2021. "Geopolitical risk and forecastability of tail risk in the oil market: Evidence from over a century of monthly data," Energy, Elsevier, vol. 235(C).
    34. Robert J. Barro, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 823-866.
    35. Ioannis Chatziantoniou & David Gabauer & Rangan Gupta, 2021. "Integration and Risk Transmission in the Market for Crude Oil: A Time-Varying Parameter Frequency Connectedness Approach," Working Papers 202147, University of Pretoria, Department of Economics.
    36. Gupta, Rangan & Huber, Florian & Piribauer, Philipp, 2020. "Predicting international equity returns: Evidence from time-varying parameter vector autoregressive models," International Review of Financial Analysis, Elsevier, vol. 68(C).
    37. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    38. Rangan Gupta & Christian Pierdzioch & Wing-Keung Wong, 2021. "A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios," Energies, MDPI, vol. 14(20), pages 1-12, October.
    39. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    40. Gabauer, David & Gupta, Rangan, 2018. "On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach," Economics Letters, Elsevier, vol. 171(C), pages 63-71.
    41. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    42. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    43. Smyth, Russell & Narayan, Paresh Kumar, 2018. "What do we know about oil prices and stock returns?," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 148-156.
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    4. 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.
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    More about this item

    Keywords

    International stock markets; Climate risks; Returns forecasting;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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