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Specification tests of parametric dynamic conditional quantiles

Author Info

Listed author(s):
  • J. Carlos Escanciano

    ()

    (Economics Department - Indiana University [Bloomington])

  • Carlos Velasco

    ()

    (Departamento de Economía. Universidad Carlos III de Madrid. Calle Madrid 126 - Departamento de Economía. Universidad Carlos III de Madrid. Calle Madrid 126)

Abstract

This article proposes omnibus specification tests of parametric dynamic quantile models. Contrary to the existing procedures, we allow for a flexible specification, where a possibly continuum of quantiles are simultaneously specified under fairly weak conditions on the serial dependence in the underlying data generating process. Since the null limit distribution of tests is not pivotal, we propose a subsampling approximation of the asymptotic critical values. A Monte Carlo study shows that the asymptotic results provide good approximations for small sample sizes. Finally, an application suggests that our methodology is a powerful alternative to standard backtesting procedures in evaluating market risk.

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File URL: https://hal.archives-ouvertes.fr/hal-00732534/document
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Paper provided by HAL in its series Post-Print with number hal-00732534.

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Date of creation: 15 Sep 2010
Publication status: Published in Journal of Econometrics, Elsevier, 2010, 159 (1), pp.209. <10.1016/j.jeconom.2010.06.003>
Handle: RePEc:hal:journl:hal-00732534
DOI: 10.1016/j.jeconom.2010.06.003
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00732534
Contact details of provider: Web page: https://hal.archives-ouvertes.fr/

Keywords: C12; C22; Omnibus tests; Conditional quantiles; Nonlinear time series; Empirical processes; Quantile processes; Subsampling; Value-at-risk; Tail risk;

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Find related papers by JEL classification:
  • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
  • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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Cited by:

  1. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
  2. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
  3. Song Xi Chen & Jiti Gao, 2010. "Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models," School of Economics Working Papers 2010-28, University of Adelaide, School of Economics.
  4. Christoph Breunig, 2016. "Specification Testing in Nonparametric Instrumental Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Qu, Zhongjun & Yoon, Jungmo, 2015. "Nonparametric estimation and inference on conditional quantile processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 1-19.
  6. Dette, Holger & Hoderlein, Stefan & Neumeyer, Natalie, 2016. "Testing multivariate economic restrictions using quantiles: The example of Slutsky negative semidefiniteness," Journal of Econometrics, Elsevier, vol. 191(1), pages 129-144.
  7. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
  8. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
  9. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
  10. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.
  11. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
  12. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
  13. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, Elsevier.
  14. So, Mike K.P. & Chung, Ray S.W., 2015. "Statistical inference for conditional quantiles in nonlinear time series models," Journal of Econometrics, Elsevier, vol. 189(2), pages 457-472.

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