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International stock market volatility: A global tail risk sight

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  • Lu, Xinjie
  • Zeng, Qing
  • Zhong, Juandan
  • Zhu, Bo

Abstract

This paper constructs a global tail risk (GTR) index and investigates the role of GTR in predicting the volatility of international stock markets. The results emphasize that GTR contains valuable information to predict the stock volatility of group (7) (G7) countries. In addition, accounting for the information of GTR and regime switching together can further improve the forecasting accuracy of international stock market volatility, especially considering the time-varying regime switching. The results are robust in different robustness checks and even during the global financial crisis period. Our paper tries to provide new evidence for tail risk in international stock market volatility prediction.

Suggested Citation

  • Lu, Xinjie & Zeng, Qing & Zhong, Juandan & Zhu, Bo, 2024. "International stock market volatility: A global tail risk sight," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
  • Handle: RePEc:eee:intfin:v:91:y:2024:i:c:s1042443123001725
    DOI: 10.1016/j.intfin.2023.101904
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    as
    1. Zhang, Yaojie & He, Jiaxin & He, Mengxi & Li, Shaofang, 2023. "Geopolitical risk and stock market volatility: A global perspective," Finance Research Letters, Elsevier, vol. 53(C).
    2. Andersen, Torben G. & Todorov, Viktor & Ubukata, Masato, 2021. "Tail risk and return predictability for the Japanese equity market," Journal of Econometrics, Elsevier, vol. 222(1), pages 344-363.
    3. Aviral Kumar Tiwari & Emmanuel Joel Aikins Abakah & OlaOluwa Simon Yaya & Kingsley Opoku Appiah, 2023. "Tail risk dependence, co-movement and predictability between green bond and green stocks," Applied Economics, Taylor & Francis Journals, vol. 55(2), pages 201-222, January.
    4. Muteba Mwamba, John W. & Hammoudeh, Shawkat & Gupta, Rangan, 2017. "Financial tail risks in conventional and Islamic stock markets: A comparative analysis," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 60-82.
    5. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    6. Hollstein, Fabian & Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "International tail risk and World Fear," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 244-259.
    7. Uddin, Gazi Salah & Rahman, Md Lutfur & Shahzad, Syed Jawad Hussain & Rehman, Mobeen Ur, 2018. "Supply and demand driven oil price changes and their non-linear impact on precious metal returns: A Markov regime switching approach," Energy Economics, Elsevier, vol. 73(C), pages 108-121.
    8. Andersen, Torben G. & Fusari, Nicola & Todorov, Viktor, 2015. "The risk premia embedded in index options," Journal of Financial Economics, Elsevier, vol. 117(3), pages 558-584.
    9. Yiannis Karavias & Paresh Kumar Narayan & Joakim Westerlund, 2023. "Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 653-666, July.
    10. Mazzarisi, Piero & Zaoli, Silvia & Campajola, Carlo & Lillo, Fabrizio, 2020. "Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    11. Luo, Changqing & Liu, Lan & Wang, Da, 2021. "Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    12. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    13. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    14. George P. Gao & Xiaomeng Lu & Zhaogang Song, 2019. "Tail Risk Concerns Everywhere," Management Science, INFORMS, vol. 65(7), pages 3111-3130, July.
    15. Li, Xiao-Ming & Rose, Lawrence C., 2009. "The tail risk of emerging stock markets," Emerging Markets Review, Elsevier, vol. 10(4), pages 242-256, December.
    16. Konstantinos Tolikas & Athanasios Koulakiotis & Richard A. Brown, 2007. "Extreme Risk and Value-at-Risk in the German Stock Market," The European Journal of Finance, Taylor & Francis Journals, vol. 13(4), pages 373-395.
    17. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    18. Qian, Lihua & Zeng, Qing & Lu, Xinjie & Ma, Feng, 2022. "Global tail risk and oil return predictability," Finance Research Letters, Elsevier, vol. 47(PB).
    19. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2023. "Tail risks and forecastability of stock returns of advanced economies: evidence from centuries of data," The European Journal of Finance, Taylor & Francis Journals, vol. 29(4), pages 466-481, March.
    20. Yu, Miao & Song, Jinguo, 2018. "Volatility forecasting: Global economic policy uncertainty and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 316-323.
    21. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    22. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    23. Chatziantoniou, Ioannis & Gabauer, David & Perez de Gracia, Fernando, 2022. "Tail risk connectedness in the refined petroleum market: A first look at the impact of the COVID-19 pandemic," Energy Economics, Elsevier, vol. 111(C).
    24. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).
    25. Ma, Feng & Liao, Yin & Zhang, Yaojie & Cao, Yang, 2019. "Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 40-55.
    26. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    27. Wagner, Niklas & Marsh, Terry A., 2005. "Measuring tail thickness under GARCH and an application to extreme exchange rate changes," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 165-185, January.
    28. Susan Chaplinsky, 2010. "Financing under Extreme Risk: Contract Terms and Returns to Private Investments in Public Equity," The Review of Financial Studies, Society for Financial Studies, vol. 23(7), pages 2789-2820, July.
    29. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    30. Zhang, Zhengyong & Shahzad, Syed Jawad Hussain & Bouri, Elie, 2022. "Tail risk transmission from commodity prices to sovereign risk of emerging economies," Resources Policy, Elsevier, vol. 78(C).
    31. Tongshuai Qiao & Liyan Han, 2023. "COVID‐19 and tail risk contagion across commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(2), pages 242-272, February.
    32. van Oordt, Maarten R. C. & Zhou, Chen, 2016. "Systematic Tail Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(2), pages 685-705, April.
    33. Hu, Yang & Lang, Chunlin & Corbet, Shaen & Hou, Yang (Greg) & Oxley, Les, 2023. "Exploring the dynamic behaviour of commodity market tail risk connectedness during the negative WTI pricing event," Energy Economics, Elsevier, vol. 125(C).
    34. Farago, Adam & Tédongap, Roméo, 2018. "Downside risks and the cross-section of asset returns," Journal of Financial Economics, Elsevier, vol. 129(1), pages 69-86.
    35. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    36. Sangwon Suh & Eungyu Yoo & Sun‐Joong Yoon, 2021. "Stock market tail risk, tail risk premia, and return predictability," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1569-1596, October.
    37. Long, Huaigang & Jiang, Yuexiang & Zhu, Yanjian, 2018. "Idiosyncratic tail risk and expected stock returns: Evidence from the Chinese stock markets," Finance Research Letters, Elsevier, vol. 24(C), pages 129-136.
    38. Bondatti, Massimiliano & Rillo, Giovanni, 2022. "Commodity tail-risk and exchange rates," Finance Research Letters, Elsevier, vol. 47(PA).
    39. Wang, Ze & Gao, Xiangyun & Huang, Shupei & Sun, Qingru & Chen, Zhihua & Tang, Renwu & Di, Zengru, 2022. "Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    40. Abbass, Kashif & Sharif, Arshian & Song, Huaming & Ali, Malik Tayyab & Khan, Farina & Amin, Nabila, 2022. "Do geopolitical oil price risk, global macroeconomic fundamentals relate Islamic and conventional stock market? Empirical evidence from QARDL approach," Resources Policy, Elsevier, vol. 77(C).
    41. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    42. Wen, Danyan & Liu, Li & Ma, Chaoqun & Wang, Yudong, 2020. "Extreme risk spillovers between crude oil prices and the U.S. exchange rate: Evidence from oil-exporting and oil-importing countries," Energy, Elsevier, vol. 212(C).
    43. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
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