Quantile forecasts of financial returns using realized garch models*

Abstract

This article applies the realized generalized autoregressive conditional heteroskedasticity (GARCH) model, which incorporates the GARCH model with realized volatility, to quantile forecasts of financial returns, such as Value-at-Risk and expected shortfall. Student's t- and skewed Student's t-distributions as well as normal distribution are used for the return distribution. The main results for the S&P 500 stock index are: (i) the realized GARCH model with the skewed Student's t-distribution performs better than that with the normal and Student's t-distributions and the exponential GARCH model using the daily returns only; and (ii) using the realized kernel to take account of microstructure noise does not improve the performance. © 2011 Japanese Economic Association.