English, Article, Journal or magazine article edition: Do high-frequency measures of volatility improve forecasts of return distributions? John M. Maheu; Thomas H. McCurdy

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Bookmark: http://trove.nla.gov.au/version/25499
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Do high-frequency measures of volatility improve forecasts of return distributions?
Author
  • John M. Maheu
  • Thomas H. McCurdy
Physical Description
  • preprint
Notes
  • 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.
  • RV, multiperiod, out-of-sample, term structure of density forecasts, observable SV
  • RePEc:tor:tecipa:tecipa-324
  • 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
  • Realized Volatility, multiperiod out-of-sample prediction, term structure of density forecasts, Stochastic Volatility
  • RePEc:rim:rimwps:wp19_09
Language
  • English
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