Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models

Zibo Niu, Chenlu Wang and Hongwei Zhang

International Review of Financial Analysis, 2023, vol. 89, issue C

Abstract: This paper investigates how geopolitical risks influence the prediction performance on the US stock market volatility with machine learning models. Further, it compares the predictive performance of individual and combination forecast methods. With SHAP algorithm, it could identify which factor has a great impact and fully extract the information of geopolitical risks in predicting. Empirical results show that military build-ups and escalation of war have great importance on predicting realized volatility among various geopolitical risks. The research further emphasizes the superior performance of machine learning and forecast combination methods, especially SVR method and trimmed mean combination. In addition, by allocating portfolio according to the machine learning-based volatility forecasts, particularly elastic net and random forest, a mean-variance investor can achieve sizeable financial benefits. Our paper provides substantial implications for political risk management and volatility forecasting.

Keywords: US stock market; Volatility forecasting; Machine learning; Combination forecast; Geopolitical risks (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:89:y:2023:i:c:s1057521923002545

DOI: 10.1016/j.irfa.2023.102738

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