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Historical geopolitical risk and the behaviour of stock returns in advanced economies

Author

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  • Afees A. Salisu
  • Lukman Lasisi
  • Jean Paul Tchankam

Abstract

In this study, we investigate the impact of global geopolitical risk (GPR) of different forms on the economies of advanced countries (G7 and Switzerland). We construct a predictive model, following the approach of Lewellen (2004. “Predicting returns with financial ratios.” Journal of Financial Economics 74 (2): 209–235) and Westerlund and Narayan (2012. “Does the choice of estimator matter when forecasting returns?” Journal of Banking & Finance 36 (9): 2632–2640; 2015. “Testing for Predictability in Conditionally Heteroskedastic Stock Returns.” Journal of Financial Econometrics 13 (2): 342–375), to analyse over a century of GPR indices and stock returns. For robustness, we control for oil price given its strong connection with stock returns of advanced economies and further extend our analysis to out-of-sample predictability. Our findings reveal that GPR is a significant predictor of stock returns in advanced economies, although their stock markets are vulnerable to GPR and particularly suffer greater adverse effects from threats of GPR (such as threats of war and terrorism) than their actual occurrence. Meanwhile, our forecast evaluation results show that the predictive model that accommodates the GPR indices outperforms the benchmark model that ignores the same both in the in-sample and out-of-sample forecast estimates.

Suggested Citation

  • Afees A. Salisu & Lukman Lasisi & Jean Paul Tchankam, 2022. "Historical geopolitical risk and the behaviour of stock returns in advanced economies," The European Journal of Finance, Taylor & Francis Journals, vol. 28(9), pages 889-906, June.
  • Handle: RePEc:taf:eurjfi:v:28:y:2022:i:9:p:889-906
    DOI: 10.1080/1351847X.2021.1968467
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    Citations

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    Cited by:

    1. António Afonso & José Alves & Sofia Monteiro, 2023. "Beyond Borders: Assessing the Influence of Geopolitical Tensions on Sovereign Risk Dynamics," CESifo Working Paper Series 10801, CESifo.
    2. Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Khan, Nasir & Saleem, Asima & Ozkan, Oktay, 2023. "Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: Evidence from novel non-parametric quantile causality approach," Resources Policy, Elsevier, vol. 81(C).
    4. Osama D. Sweidan, 2023. "Geopolitical Risk and Income Inequality: Evidence from the US Economy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 169(1), pages 575-597, September.
    5. Kamal, Md Rajib & Wahlstrøm, Ranik Raaen, 2023. "Cryptocurrencies and the threat versus the act event of geopolitical risk," Finance Research Letters, Elsevier, vol. 57(C).
    6. Afees A. Salisu & Philip C. Omoke & Abdulsalam Abidemi Sikiru, 2023. "Geopolitical risk and global financial cycle: Some forecasting experiments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 3-16, January.
    7. Khalfaoui, Rabeh & Gozgor, Giray & Goodell, John W., 2023. "Impact of Russia-Ukraine war attention on cryptocurrency: Evidence from quantile dependence analysis," Finance Research Letters, Elsevier, vol. 52(C).
    8. Yu Song & Bo Chen & Xin-Yi Wang, 2023. "Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-20, December.
    9. Kamal, Md Rajib & Ahmed, Shaker & Hasan, Mostafa Monzur, 2023. "The impact of the Russia-Ukraine crisis on the stock market: Evidence from Australia," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).

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