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The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility

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  • Byun, Sung Je

Abstract

Does cross-sectional dispersion in the returns of different stocks help forecast volatility of the S&P 500 index? This paper develops a model of stock returns where dispersion in returns across different stocks is modeled jointly with aggregate volatility. Although specifications that allow for feedback from cross-sectional dispersion to aggregate volatility have a better fit in sample, they prove not to be robust for purposes of out-of-sample forecasting. Using a full cross-section of stock returns jointly, however, I find that use of cross-sectional dispersion can help improve parameter estimates of a GARCH process for aggregate volatility to generate better forecasts both in sample and out of sample. Given this evidence, I conclude that cross-sectional information helps predict market volatility indirectly rather than directly entering in the data-generating process.

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  • Byun, Sung Je, 2016. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 162-180.
    • Handle: RePEc:eee:empfin:v:36:y:2016:i:c:p:162-180
    DOI: 10.1016/j.jempfin.2016.01.013
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    Cited by:

    1. Sung Je Byun & Soojin Jo, 2018. "Heterogeneity in the dynamic effects of uncertainty on investment," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(1), pages 127-155, February.
    2. S. Al Wadi, 2017. "Improving Volatility Risk Forecasting Accuracy in Industry Sector," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2017, pages 1-6, November.
    3. Claudiu Vinte & Marcel Ausloos, 2022. "The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator," Papers 2205.00104, arXiv.org.
    4. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    5. Fei, Tianlun & Liu, Xiaoquan & Wen, Conghua, 2019. "Cross-sectional return dispersion and volatility prediction," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).

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    More about this item

    Keywords

    Forecasting S&P 500 volatility; Cross-sectional dispersion; Aggregate idiosyncratic volatility; Large panel data model;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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