CFDP 1679

Copula-Based Nonlinear Quantile Autoregression


Publication Date: October 2008

Pages: 30


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.


Quantile autoregression, Copula, Ergodic nonlinear Markov models

JEL Classification Codes: C22, C63

See CFP: 1281