Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence

Smith, Michael S.; Min, Aleksey; Almeida, Carlos; Czado, Claudia

doi:10.1198/jasa.2010.tm09572

Cited by 160 publications

(161 citation statements)

References 33 publications

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“…At the moment only selection procedures within specified D-vine structures exist. Min and Czado (2010b) use reversible jump MCMC to simplify a D-vine with specific single pair-copula family by discovering conditional independences, while Smith et al (2010) use indicator variables for identifying conditional independence in a Bayesian setup. The goal of this paper is to provide a comprehensive solution to the selection of C-vines by identifying an appropriate C-vine structure and selecting a fitting pair-copula family.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation of mixed C-vines with application to exchange rates

Czado

Schepsmeier

Min

2012

Statistical Modelling

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Multivariate copulas are commonly used in economics, finance and risk management. They allow for very flexible dependency structures, even though they are applied to transformed financial data after marginal time dependencies are removed. This is necessary to facilitate statistical parameter estimation. In this paper we consider a very flexible class of mixed C-vines, which allows the variables to be ordered according to their influence. Vines are build from bivariate copulas only and the term "mixed" refers to allowing the pair-copula family to be chosen individually for each term. In addition there are many C-vine structure specifications possible and therefore we propose a novel data driven sequential selection procedure, which selects both the C-vine structure and its attached pair-copula families with parameters. After the model selection ML estimation of the parameters is facilitated using the above found sequential estimates as starting values. An extensive simulation study shows a satisfactory performance of ML estimates in small samples. Finally an application involving US-exchange rates demonstrates the need for mixed C-vine models.

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Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation of mixed C-vines with application to exchange rates

Czado

Schepsmeier

Min

2012

Statistical Modelling

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183

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“…A goodness of fit test can be performed to choose the combination of the pair-copulae that best fits to data. Smith et al (2010) , Smith (2015) and Panagiotelis, Czado, and Harry (2012) use a vinecopula approach to model the dependence structure for longitudinal data and have shown encouraging results on the goodness of fit achieved in an empirical analysis on headache severity. Different vine decomposition, such as regular-vine (R-vine), canonicalvine (C-vine) and drawable vine (D-vine), have been proposed to describe specific dependence between the variables.…”

Section: The D-vine Copulamentioning

confidence: 99%

“…For modelling the time dependence in longitudinal data, in this paper we choose a copula approach, analogously to Smith, Min, Almeida, and Czado (2010) and Meade and Islam (2010) , because it is a simple method to estimate multivariate models when the marginal responses are given. Furthermore, the copula approach is particularly suitable to model different kinds of dependence structures.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we consider a drawable vine (D-vine) copula as it is an extremely flexible representation of a multivariate distribution that uses bivariate copula densities (pair-copula) in a hierarchical approach as in Smith et al (2010) and Smith (2015) . In the D-vine copula approach, the multivariate probability density function is decomposed into pair-copulae that can be chosen independently from each other, generating a less flexible multivariate dependence structure.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, the multivariate copula models largely used in the literature, such as multivariate Archimedian, Gaussian, Gumbel, Clayton and Joe, have the same dependence structure for each pair-copulae. Moreover, the D-vine copula approach is suitable for modelling longitudinal data and to represent the dependence structure over time ( Smith, 2015;Smith et al, 2010 ). For all these reasons, we use a D-vine copula approach to model the time series of the failure probabilities, previously estimated by using the GEV model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The effectiveness of TARP-CPP on the US banking industry: A new copula-based approach

Calabrese

Degl’Innocenti

Osmetti

2017

European Journal of Operational Research

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a b s t r a c tFollowing the 2008 financial crisis, regulatory authorities and governments provided distressed banks with equity infusions in order to strengthen national banking systems. However, the effectiveness of these interventions for financial stability has not been extensively researched in the literature. In order to understand the effectiveness of these bailouts for the solvency of banks this paper proposes a new model: the Longitudinal Binary Generalised Extreme Value (LOBGEV) model. Differing from the existing models, the LOBGEV model allows us to analyse the temporal structure of the probability of failure for banks, for both those that received a bailout and for those that did not. In particular, it encompasses both the flexibility of the D-vine copula and the accuracy of the generalised extreme value model in estimating the probability of bank failure and of banks receiving approval for capital injection. We apply this new model to the US banking system from 2008 to 2013 in order to investigate how and to what extent the Troubled Asset Relief Program (TARP)-Capital Purchase Program (CPP) reduced the probability of the failure of commercial banks. We specifically identify a set of macroeconomic and bank-specific factors that affect the probability of bank failure for TARP-CCP recipients and for those that did not receive capital under TARP-CCP. Our results suggest that TARP-CPP provided only short-term relief for US commercial banks.

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COPAR—multivariate time series modeling using the copula autoregressive model

Brechmann

Czado

2014

Appl Stoch Models Bus & Ind

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The analysis of multivariate time series is a common problem in areas like finance and economics. The classical tools for this purpose are vector autoregressive models. These however are limited to the modeling of linear and symmetric dependence. We propose a novel copula‐based model that allows for the non‐linear and non‐symmetric modeling of serial as well as between‐series dependencies. The model exploits the flexibility of vine copulas, which are built up by bivariate copulas only. We describe statistical inference techniques for the new model and discuss how it can be used for testing Granger causality. Finally, we use the model to investigate inflation effects on industrial production, stock returns and interest rates. In addition, the out‐of‐sample predictive ability is compared with relevant benchmark models. Copyright © 2014 John Wiley & Sons, Ltd.

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Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence

Cited by 160 publications

References 33 publications

Maximum likelihood estimation of mixed C-vines with application to exchange rates

Maximum likelihood estimation of mixed C-vines with application to exchange rates

The effectiveness of TARP-CPP on the US banking industry: A new copula-based approach

COPAR—multivariate time series modeling using the copula autoregressive model

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