Do high-frequency measures of volatility improve forecasts of return distributions?
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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.
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