Forecasting the Return Distribution Using High-Frequency Volatility Measures
41 Pages Posted: 27 Oct 2011 Last revised: 22 Sep 2013
Date Written: October 25, 2011
Abstract
The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns at the 1 and 2 day, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH(1,1) model when the 30-year Treasury bond futures return is considered.
Keywords: Realized Volatility, Quantile Regression, Density Forecast, Value-at-Risk
JEL Classification: C14, C22, C53
Suggested Citation: Suggested Citation
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