Copula-based nonlinear quantile autoregression

Xiaohong Chen, Roger Koenker, Zhijie Xiao

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)S50-S67
JournalEconometrics Journal
Issue numberSUPPL. 1
StatePublished - 2009
Externally publishedYes


  • Copula
  • Ergodic nonlinear Markov models
  • Quantile autoregression

ASJC Scopus subject areas

  • Economics and Econometrics


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