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Copula-based nonlinear quantile autoregression


  • Xiaohong Chen

    () (Institute for Fiscal Studies and Yale University)

  • Roger Koenker

    () (Institute for Fiscal Studies and University of Illinois)

  • Zhijie Xiao

    (Institute for Fiscal Studies)


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.

Suggested Citation

  • Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2008. "Copula-based nonlinear quantile autoregression," CeMMAP working papers CWP27/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:27/08

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    References listed on IDEAS

    1. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
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    Cited by:

    1. repec:eee:agiwat:v:191:y:2017:i:c:p:153-172 is not listed on IDEAS
    2. Beare, Brendan K., 2012. "Archimedean Copulas And Temporal Dependence," Econometric Theory, Cambridge University Press, vol. 28(06), pages 1165-1185, December.
    3. Xiaohong Chen & Wei Biao Wu & Yanping Yi, 2009. "Efficient Estimation of Copula-based Semiparametric Markov Models," Cowles Foundation Discussion Papers 1691, Cowles Foundation for Research in Economics, Yale University, revised Mar 2009.
    4. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
    5. Baur, Dirk G., 2013. "The structure and degree of dependence: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 786-798.
    6. Beare, Brendan K. & Seo, Juwon, 2014. "Time Irreversible Copula-Based Markov Models," Econometric Theory, Cambridge University Press, vol. 30(05), pages 923-960, October.
    7. Sim, Nicholas, 2016. "Modeling the dependence structures of financial assets through the Copula Quantile-on-Quantile approach," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 31-45.
    8. Julio Galvez & Javier MencĂ­a, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
    9. Li, Haiqi & Zheng, Chaowen & Guo, Yu, 2016. "Estimation and test for quantile nonlinear cointegrating regression," Economics Letters, Elsevier, vol. 148(C), pages 27-32.
    10. Jin, Xisong & Nadal De Simone, Francisco de A., 2014. "Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 81-101.
    11. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, Elsevier.
    12. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.

    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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