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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. 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.
    2. 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).
    3. 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.

    More about this item

    Keywords

    Geopolitical risks; Volatility forecasts; Markov-switching GARCH-MIDAS;
    All these keywords.

    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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