Working Paper

Copula-based nonlinear quantile autoregression

Authors

Xiaohong Chen, Roger Koenker, Zhijie Xiao

Published Date

23 October 2008

Type

Working Paper (CWP27/08)

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.


Latest version

Copula-based nonlinear quantile autoregression
Xiaohong Chen, Roger Koenker, Zhijie Xiao
CWP