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

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  • John M Maheu
  • Thomas H McCurdy

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.

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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-324.

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Length: 31 pages
Date of creation: 06 Aug 2008
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-324

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Keywords: RV; multiperiod; out-of-sample; term structure of density forecasts; observable SV;

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References

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