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

Posted: 5 Oct 2011

See all articles by John M. Maheu

John M. Maheu

McMaster University - Michael G. DeGroote School of Business; RCEA

Thomas H. McCurdy

University of Toronto - Rotman School of Management

Multiple version iconThere are 2 versions of this paper

Date Written: June 1, 2006

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.

Keywords: Realized volatility, multiperiod out-of-sample prediction, term structure of density forecasts, stochastic volatility

Suggested Citation

Maheu, John M. and McCurdy, Thomas H., Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (June 1, 2006). Journal of Econometrics, Vol. 160, No. 1, 2011, Available at SSRN: https://ssrn.com/abstract=1939398

John M. Maheu

McMaster University - Michael G. DeGroote School of Business ( email )

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Hamilton, Ontario L8S 4M4
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RCEA

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Thomas H. McCurdy (Contact Author)

University of Toronto - Rotman School of Management ( email )

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Canada
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416-971-3048 (Fax)

HOME PAGE: http://www-2.rotman.utoronto.ca/~tmccurdy

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