Copula-based nonlinear quantile autoregression
提出用参数Copula构建非线性时间序列的分位数自回归模型，估计方法在模型设定错误时仍保持一致性，适用于金融数据的极端条件风险价值推断。
Parametric copulas are shown to be attractive devices for specifying quantile autoregressive models for nonlinear time‐series. Estimation of local, quantile‐specific copula‐based time series models offers some salient advantages over classical global parametric approaches. Consistency and asymptotic normality of the proposed quantile estimators are established under mild conditions, allowing for global misspecification of parametric copulas and marginals, and without assuming any mixing rate condition. These results lead to a general framework for inference and model specification testing of extreme conditional value‐at‐risk for financial time series data.