Conditional Quantile Analysis for Realized GARCH Models

This paper introduces a novel quantile approach to harness the high-frequency information and improve daily conditional estimation. Specifically, we model standard deviation as realized GARCH employ deviation, volatility, quantile, absolute overnight return innovations in proposed dynamic models. We devise two-step estimation procedure estimate parameters. The first step applies quasi-maximum likelihood procedure, with volatility proxy for proxy, second utilizes regression estimated step. Asymptotic theory is established methods, simulation study conducted check their finite-sample performance. Finally, apply methodology calculate value at risk (VaR) of 20 individual assets compare its performance existing competitors.