Artikel
Goodness-of-fit tests for copulas of multivariate time series
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 5 ; Year: 2017 ; Issue: 1 ; Pages: 1-23 ; Basel: MDPI
- Classification
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Wirtschaft
Hypothesis Testing: General
Semiparametric and Nonparametric Methods: General
Statistical Simulation Methods: General
Financial Econometrics
- Subject
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goodness-of-fit
time series
copulas
GARCH models
- Event
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Geistige Schöpfung
- (who)
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Rémillard, Bruno
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2017
- DOI
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doi:10.3390/econometrics5010013
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
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Object type
- Artikel
Associated
- Rémillard, Bruno
- MDPI
Time of origin
- 2017
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