Do high-frequency measures of volatility improve forecasts of return distributions?

Working Papers from University of Toronto, Department of Economics

Abstract: Many finance questions require a full characterization of the distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns.

Keywords: RV; multiperiod; out-of-sample; term structure of density forecasts; observable SV (search for similar items in EconPapers)
JEL-codes: C1 C32 C50 G1 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2008-08-06
New Economics Papers: this item is included in nep-ets, nep-for, nep-mst and nep-rmg
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Do high-frequency measures of volatility improve forecasts of return distributions? (2011)
Working Paper: Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-324

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