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

中文翻译：

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已实现 GARCH 模型的条件分位数分析

本文介绍了一种新颖的分位数方法来利用高频信息并改进每日条件分位数估计。具体来说，我们将条件标准差建模为已实现的广义自回归条件异方差 (GARCH) 模型，并采用条件标准差、已实现波动率、已实现分位数和绝对隔夜收益率作为所提出的动态分位数模型的创新。我们设计了一个两步估计程序来估计条件分位数参数。第一步应用准最大似然估计程序，以已实现的波动率作为波动率代理的代理，以估计条件标准差参数。第二步使用分位数回归估计程序，在第一步中估计条件标准偏差。为所提出的估计方法建立了渐近理论，并进行了模拟研究以检查它们的有限样本性能。最后，我们应用所提出的方法来计算 20 种单独资产的风险价值，并将其性能与现有竞争对手进行比较。