Copula-Based Nonlinear Quantile Autoregression

Xiaohong Chen (), Roger Koenker and Zhijie Xiao
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Roger Koenker: Dept. of Economics, University of Illinois at Urbana-Champaign

No 1679, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Keywords: Quantile autoregression; Copula; Ergodic nonlinear Markov models (search for similar items in EconPapers)
JEL-codes: C22 C63 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2008-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (2)

Published in Econometrics Journal (January 2009), 12(1): S50-S67

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Journal Article: Copula-based nonlinear quantile autoregression (2009)
Working Paper: Copula-Based Nonlinear Quantile Autoregression (2008)
Working Paper: Copula-based nonlinear quantile autoregression (2008)
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