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Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?

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  • John M. Maheu

    ()
    ( Department of Economics, University of Toronto and RCEA)

  • Thomas H. McCurdy

    ()
    ( Rotman School of Management, University of Toronto, and CIRANO)

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

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 19_09.

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Date of creation: Jan 2009
Date of revision: Jan 2009
Handle: RePEc:rim:rimwps:19_09

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Keywords: Realized Volatility; multiperiod out-of-sample prediction; term structure of density forecasts; Stochastic Volatility;

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References

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