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Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks

Author

Listed:
  • Mawuli Segnon

    (Department of Economics, Institute for Econometric and Economic Statistics and Chair of Empirical Economics, University of Munster, Germany)

  • Rangan Gupta

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

  • Bernd Wilfling

    (Westfalische Wilhelms-Universitat Munster, Department of Economics (CQE), Germany)

Abstract

We investigate the role of geopolitical risks (GPR) in forecasting stock market volatility in a robust autoregressive Markov-switching GARCH mixed data sampling (ARMSGARCH-MIDAS) framework that accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components driven by geopolitical risks. An empirical out-of-sample forecasting exercise is conducted using unique data sets on Dow Jones Industrial Average (DJIA) index and geopolitical risks that cover the time period from January 3, 1899 to December 31, 2020. We find that geopolitical risks as explanatory variables can help to improve the accuracy of stock market volatility forecasts. Furthermore, our empirical results show that the macroeconomic variables such as output measured by recessions, inflation and interest rates contain information that is complementary to the one included in the geopolitical risks.

Suggested Citation

  • Mawuli Segnon & Rangan Gupta & Bernd Wilfling, 2022. "Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks," Working Papers 202203, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202203
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    Cited by:

    1. Mensi, Walid & Lee, Yeonjeong & Al-Kharusi, Sami & Yoon, Seong-Min, 2024. "Switching spillovers and connectedness between Sukuk and international Islamic stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    2. Cai, Yifei & Fu, Xiaowen & Zhang, Yahua, 2025. "Geopolitical risks and airlines stock return — Implications to the financial stability of European airlines," Transport Policy, Elsevier, vol. 170(C), pages 51-57.
    3. Peng, Lijuan & Liang, Chao & Yang, Baoying & Wang, Lu, 2024. "Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 94(C).
    4. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2024. "The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model," Papers 2404.01641, arXiv.org.
    5. V. Candila & O. Cepni & G. M. Gallo & R. Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the volatility of US state-level equity returns: Evidence from a GARCH-MIDAS approach with Shrinkage and Cluster Analysis," Working Paper CRENoS 202414, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    6. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2025. "The Role of Uncertainty in Forecasting Realized Covariance of US State-Level Stock Returns: A Reverse-MIDAS Approach," Working Papers 202501, University of Pretoria, Department of Economics.
    7. Neto, David, 2025. "Wall Street sneezes and global finance catches a cold: How does geopolitical risk contribute? A tale of tail," Finance Research Letters, Elsevier, vol. 73(C).
    8. Mo, Bin & Chen, Jiaru & Shi, Qinling & Zeng, Zichun, 2025. "Cryptocurrencies as safe havens for geopolitical risk? A quantile analysis approach," The North American Journal of Economics and Finance, Elsevier, vol. 79(C).
    9. Afees A. S alisu & Wenting Liao & Rangan Gupta & Oguzhan Cepni, 2025. "Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH‐MIDAS Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1441-1466, July.
    10. Salisu, Afees A. & Demirer, Riza & Gupta, Rangan, 2024. "Technological shocks and stock market volatility over a century," Journal of Empirical Finance, Elsevier, vol. 79(C).
    11. Liu, Zhenhua & Wang, Yushu & Yuan, Xinting & Ding, Zhihua & Ji, Qiang, 2025. "Geopolitical risk and vulnerability of energy markets," Energy Economics, Elsevier, vol. 141(C).
    12. Yun‐Shi Dai & Peng‐Fei Dai & Wei‐Xing Zhou, 2025. "Geopolitical Risk and the Volatility of the International Grain Futures Market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(10), pages 1757-1794, October.
    13. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Bouri, Elie, 2024. "Energy-related uncertainty and international stock market volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 280-293.
    14. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    15. Afees A. Salisu & Ahamuefula E.Oghonna & Rangan Gupta & Oguzhan Cepni, 2024. "Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach," Working Papers 202409, University of Pretoria, Department of Economics.

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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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