Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements
提出一种新框架，利用已实现波动率测度来估计和预测极端分位数，通过结合分位数回归与极值理论，并引入测量方程连接已实现测度与潜在条件分位数，实证表明高频数据对动态分位数有信息价值，样本外预测验证了模型在风险度量中的优势。
Summary We propose a new framework exploiting realized measures of volatility to estimate and forecast extreme quantiles. Our realized extreme quantile (REQ) combines quantile regression with extreme value theory and uses a measurement equation that relates the realized measure to the latent conditional quantile. Model estimation is performed by quasi maximum likelihood, and a simulation experiment validates this estimator in finite samples. An extensive empirical analysis shows that high‐frequency measures are particularly informative of the dynamic quantiles. Finally, an out‐of‐sample forecast analysis of quantile‐based risk measures confirms the merit of the REQ.