The Grouped t-Copula with an Application to Credit Risk

Daul, Stéphane; Giorgi, Enrico G. De; Lindskog, Filip; McNeil, Alexander J.

doi:10.2139/ssrn.1358956

Cited by 48 publications

(58 citation statements)

References 6 publications

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“…Partition { , ..., n} into m subsets of sizes s , ..., sm. Let The grouped t-copula is described in more detail in [19]. For calibration of and simulation from the grouped t-copula, there is no need for an explicit copula expression.…”

Section: Dynamic Grouped T-copulamentioning

confidence: 99%

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

Jin

Lehnert

2018

Dependence Modeling

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Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) for passive portfolios and dynamic optimal portfolios using Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1) Modeling the marginal distribution is important for dynamic high-dimensional multivariate models. (2) Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3) The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4) Grouped t-copulas and t-copulas with dynamic degrees of freedom further match the fat tail. (5) Correctly modeling the dependence structure makes an improvement in portfolio optimization with respect to tail risk. (6) Models driven by multivariate t innovations with exogenously given degrees of freedom provide a exible and applicable alternative for optimal portfolio risk management.

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Section: Dynamic Grouped T-copulamentioning

confidence: 99%

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

Jin

Lehnert

2018

Dependence Modeling

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“…A slightly different definition of the grouped t copula with constant correlation matrix R t = R was proposed by Daul et al (2003) and its properties were studied by Demarta and McNeil (2005). In their model, the inverse gamma mixing variables are generated from a common uniform random variable at each date.…”

Section: Conditionally Grouped Student's T Factor Copulamentioning

confidence: 99%

High dimensional dynamic stochastic copula models

Creal

Tsay

2015

Journal of Econometrics

101

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“…After Li (2000), the literature dealing with the use of copulas in credit risk has been focused on corporate debts (Das and Geng, 2006;Hamerle and Rösch, 2005;Hamilton et al, 2001), derivatives (Cherubini et al, 2004;Hull and White, 2006;Melchiori, 2003), general theoretical models (Kostadinov, 2005;Schönbucher and Schubert, 2001) and the relationship between risk factors and defaults (Daul et al, 2003;Schmidt, 2003). The most frequently considered copulas have been the elliptical (mainly Gaussian and Student t) and the Archimedean (especially Clayton, Frank, and Gumbel).…”

Section: Application Of Copulas To Credit Riskmentioning

confidence: 99%

Checking for asymmetric default dependence in a credit card portfolio: A copula approach

Crook

Moreira

2011

Journal of Empirical Finance

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Traditional credit risk models adopt the linear correlation as a measure of dependence and assume that credit losses are normally-distributed. However some studies have shown that credit losses are seldom normal and the linear correlation does not give accurate assessment for asymmetric data. Therefore it is possible that many credit models tend to misestimate the probability of joint extreme defaults. This paper employs Copula Theory to model the dependence across default rates in a credit card portfolio of a large UK bank and to estimate the likelihood of joint high default rates. Ten copula families are used as candidates to represent the dependence structure. The empirical analysis shows that, when compared to traditional models, estimations based on asymmetric copulas usually yield results closer to the ratio of simultaneous extreme losses observed in the credit card portfolio. Copulas have been applied to evaluate the dependence among corporate debts but this research is the first paper to give evidence of the outperformance of copula estimations in portfolios of consumer loans. Moreover we test some families of copulas that are not typically considered in credit risk studies and find out that three of them are suitable for representing dependence across credit card defaults.

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The Grouped t-Copula with an Application to Credit Risk

Cited by 48 publications

References 6 publications

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

High dimensional dynamic stochastic copula models

Checking for asymmetric default dependence in a credit card portfolio: A copula approach

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