Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification

Estimation and model selection of semiparametric copula-based multivariate dynamic models under... We introduce a new class of semiparametric copula-based multivariate dynamic (SCOMDY) models, which specify the conditional mean and the conditional variance of a multivariate time series parametrically, but specify the multivariate distribution of the standardized innovation semiparametrically as a parametric copula evaluated at nonparametric marginal distributions. We first study large sample properties of the estimators of SCOMDY model parameters under a misspecified parametric copula, then propose pseudo likelihood ratio (PLR) tests for model selection between two SCOMDY models with possibly misspecified copulas, and finally develop PLR tests for model selection between more than two SCOMDY models. The limiting null distributions of the PLR tests do not depend on the estimation of conditional mean and conditional variance parameters, hence are very easy to simulate. Empirical applications to three and higher dimensional daily exchange rate series indicate that a SCOMDY model with a tail-dependent copula is generally preferred. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Econometrics Elsevier

Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification

Journal of Econometrics, Volume 135 (1) – Nov 1, 2006

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Publisher
Elsevier
Copyright
Copyright © 2005 Elsevier B.V.
ISSN
0304-4076
eISSN
1872-6895
D.O.I.
10.1016/j.jeconom.2005.07.027
Publisher site
See Article on Publisher Site

Abstract

We introduce a new class of semiparametric copula-based multivariate dynamic (SCOMDY) models, which specify the conditional mean and the conditional variance of a multivariate time series parametrically, but specify the multivariate distribution of the standardized innovation semiparametrically as a parametric copula evaluated at nonparametric marginal distributions. We first study large sample properties of the estimators of SCOMDY model parameters under a misspecified parametric copula, then propose pseudo likelihood ratio (PLR) tests for model selection between two SCOMDY models with possibly misspecified copulas, and finally develop PLR tests for model selection between more than two SCOMDY models. The limiting null distributions of the PLR tests do not depend on the estimation of conditional mean and conditional variance parameters, hence are very easy to simulate. Empirical applications to three and higher dimensional daily exchange rate series indicate that a SCOMDY model with a tail-dependent copula is generally preferred.

Journal

Journal of EconometricsElsevier

Published: Nov 1, 2006

References

  • On the out-of-sample importance of skewness and asymmetric dependence for asset allocation
    Patton, A.
  • Information criteria for selecting possibly misspecified parametric models
    Sin, C.; White, H.
  • Encompassing tests when no model is encompassing
    West, K.

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