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Forecasting Sector-Level Stock Market Volatility: The Role of World Uncertainty Index

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  • Yu, Miao

Abstract

This study investigates the predictive ability of the World Uncertainty Index (WUI) on forecasting sector-level stock market volatility in the Shanghai Stock Exchange. The out-of-sample results demonstrate that the WUI improves volatility predictions, particularly in the materials, industrials, and health care sectors. Further analysis reveals the WUI's stronger predictive capability in high volatility states. The findings highlight the value of the WUI as a tool for anticipating and managing risk in specific sectors of the stock market.

Suggested Citation

  • Yu, Miao, 2023. "Forecasting Sector-Level Stock Market Volatility: The Role of World Uncertainty Index," Finance Research Letters, Elsevier, vol. 58(PC).
  • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323009406
    DOI: 10.1016/j.frl.2023.104568
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    References listed on IDEAS

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    1. N. Bloom, 2016. "Fluctuations in uncertainty," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 4.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. George Bittlingmayer, 1998. "Output, Stock Volatility, and Political Uncertainty in a Natural Experiment: Germany, 1880-1940," Journal of Finance, American Finance Association, vol. 53(6), pages 2243-2257, December.
    4. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A comprehensive look at financial volatility prediction by economic variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 956-977, September.
    5. Amengual, Dante & Xiu, Dacheng, 2018. "Resolution of policy uncertainty and sudden declines in volatility," Journal of Econometrics, Elsevier, vol. 203(2), pages 297-315.
    6. Chau, Frankie & Deesomsak, Rataporn & Wang, Jun, 2014. "Political uncertainty and stock market volatility in the Middle East and North African (MENA) countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 1-19.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Ma, Feng & Guo, Yangli & Chevallier, Julien & Huang, Dengshi, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," International Review of Financial Analysis, Elsevier, vol. 84(C).
    9. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    10. Chowdhury, Mohammad Ashraful Ferdous & Meo, Muhammad Saeed & Aloui, Chaker, 2021. "How world uncertainties and global pandemics destabilized food, energy and stock markets? Fresh evidence from quantile on quantile regressions," International Review of Financial Analysis, Elsevier, vol. 76(C).
    11. Feng Ma & Yangli Guo & Julien Chevallier & Dengshi Huang, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," Post-Print halshs-04250304, HAL.
    Full references (including those not matched with items on IDEAS)

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