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Copula-Based Nonlinear Quantile Autoregression

Xiaohong ChenRoger KoenkerZhijie Xiao

出版	Boston College, Department of Economics, 2008
URL	http://books.google.com.hk/books?id=kkYxMwEACAAJ&hl=&source=gbs_api

註釋

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.