Goodness-of-Fit Tests for Copulas of Multivariate Time Series

By Bruno R'emillard. 2017

82 citations recorded by the source corpus.

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/fb9a96eb063b65f3707e837f6fc6e4fb6df8efaa

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Citations in the corpus

Modeling Dependence in High Dimensions With Factor Copulas (2015, methodology): “The class of data-generating processes (DGPs) covered by Oh and Patton (2013) is the same as that in Chen and Fan (2006) and Rémillard (2010).”
Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads (2018, unknown): “In contrast, Chen and Fan (2006), Rémillard (2010), and Oh and Patton (2013) allowed for nonparametric estimation of the marginal distributions.”