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

John Maheu and Thomas McCurdy

Working Paper series from Rimini Centre for Economic Analysis

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 (search for similar items in EconPapers)
Date: 2009-01
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Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Do high-frequency measures of volatility improve forecasts of return distributions? (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:19_09

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