Goodness-of-fit test for specification of semiparametric copula dependence models

Shulin Zhang, Ostap Okhrin, Qian M. Zhou and Peter X.-K. Song

No 2013-041, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: This paper concerns goodness-of-fit test for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between in-sample and out-of-sample pseudolikelihoods, which avoids the use of any probability integral transformations. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space and establish the asymptotic normality for the test. Second, we introduce a hybrid mechanism to combine several test statistics, so that the resulting test will make a desirable test power among the involved tests. This hybrid method is particularly appealing when there exists no single dominant optimal test. We conduct comprehensive simulation experiments to compare the proposed new test and hybrid approach with the best blank test shown in Genest et al. (2009). For illustration, we apply the proposed tests to analyze three real datasets.

Keywords: hybrid test; in-and-out-of sample likelihood; power; tail dependence (search for similar items in EconPapers)
JEL-codes: C12 C22 C32 C52 G15 (search for similar items in EconPapers)
Date: 2013
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Journal Article: Goodness-of-fit test for specification of semiparametric copula dependence models (2016)
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