Hybrid Quantile Regression Estimation for Time Series Models with Conditional Heteroscedasticity
条件异方差时间序列模型的混合分位数回归估计
Summary Estimating conditional quantiles of financial time series is essential for risk management and many other financial applications. For time series models with conditional heteroscedasticity, although it is the generalized auto-regressive conditional heteroscedastic (GARCH) model that has the greatest popularity, quantile regression for this model usually gives rise to non-smooth non-convex optimization which may hinder its practical feasibility. The paper proposes an…