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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.

Suggested Citation

  • John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
  • Handle: RePEc:tor:tecipa:tecipa-324
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    References listed on IDEAS

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    More about this item

    Keywords

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

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G1 - Financial Economics - - General Financial Markets

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