The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility |
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| Institution: | 1. Department of Economics and Regional Development, Panteion University of Social and Political Sciences, 136 Syggrou Avenue, 17671, Greece;2. Postgraduate Department of Business Administration, Hellenic Open University, Aristotelous 18, 26 335, Greece;3. Bournemouth University, Department of Accounting, Finance and Economics, Executive Business Centre, 89 Holdenhurst Road, BH8 8EB, Bournemouth, UK;4. Research Institute of Energy Management and Planning, University of Tehran, 13 Ghods St., Enghelab Ave., Tehran, Iran;1. Department of Economics, University of Peloponnese, Greece;2. Department of Accounting and Finance, Athens University of Economics and Business, Greece;3. EDHEC Business School and EDHEC Risk Institute, France;1. College of Business, Zayed University, MF2-2-007, Abu Dhabi - Khalifa City, United Arab Emirates;2. Department of Accounting and Finance, University of Thessaly, Gaiopolis Campus, Larissa, 41500, Greece;1. HEC Montreal, Canada;2. UNSW Business School, University of New South Wales, Australia |
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| 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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