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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

Suggested Citation

  • John M. Maheu & Thomas H. McCurdy, 2009. "Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?," Working Paper series 19_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:19_09
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    More about this item

    Keywords

    Realized Volatility; multiperiod out-of-sample prediction; term structure of density forecasts; Stochastic Volatility;
    All these keywords.

    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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