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Cross-sectional uncertainty and stock market volatility: New evidence

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  • Lu, Fei
  • Ma, Feng

Abstract

This study investigates the potential of cross-sectional uncertainty (CSU) to predict stock market volatility. Empirical findings reveal that the newly developed variance-based index conditioned on economic policy uncertainty exhibits greater predictive power than the widely used economic policy uncertainty index. Sparse methods consider multiple predictors and perform well. Further research has demonstrated that the CSU index contains more valuable information and delivers better predictive performance and economic value, especially under financial crises. Our study extends the application of the CSU index and provides novel evidence for volatility prediction.

Suggested Citation

  • Lu, Fei & Ma, Feng, 2023. "Cross-sectional uncertainty and stock market volatility: New evidence," Finance Research Letters, Elsevier, vol. 57(C).
  • Handle: RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323005743
    DOI: 10.1016/j.frl.2023.104202
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    References listed on IDEAS

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    1. Ian Dew-Becker & Stefano Giglio, 2023. "Cross-Sectional Uncertainty and the Business Cycle: Evidence from 40 Years of Options Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 65-96, April.
    2. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    3. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    4. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    5. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    6. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    7. 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.
    8. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    9. 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.
    10. Zeng, Qing & Ma, Feng & Lu, Xinjie & Xu, Weiju, 2022. "Policy uncertainty and carbon neutrality: Evidence from China," Finance Research Letters, Elsevier, vol. 47(PB).
    11. Fenghua Wen & Yupei Zhao & Minzhi Zhang & Chunyan Hu, 2019. "Forecasting realized volatility of crude oil futures with equity market uncertainty," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6411-6427, December.
    12. Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
    13. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
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