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

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  • Maheu, John M.
  • McCurdy, Thomas H.

Abstract

Many finance questions require the predictive 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

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 160 (2011)
Issue (Month): 1 (January)
Pages: 69-76

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Handle: RePEc:eee:econom:v:160:y:2011:i:1:p:69-76

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Realized volatility Multiperiod out-of-sample prediction Term structure of density forecasts Stochastic volatility;

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