Abstract
Over the last couple of years we could observe a strong growth of copula based credit portfolio models. So far the major interest has revolved the ability of certain copula families to map specific phenomena such as default clustering or the evolution of prices (e.g., credit derivatives prices). Still few questions have been posed regarding copula selection. This is surprising as the problem of estimating the dependence structure is even unresolved with simple traditional models. For statistical tests of credit portfolio models in general the literature found densitybased tests like that of Berkowitz (2001) the most reasonable option. In this text, we examine its power characteristics concerning factor portfolio models in more detail. Our results suggest that both the copula family as well as the level of dependence is generally very difficult to identify.
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References
Aas, K. (2004). Modelling the dependence structure of financial assets: a survey of four copulas, Technical report, Norwegian Computing Center.
Berkowitz, J. (2001). Testing density forecasts, with applications to risk management, Journal of Business and Economic Statistics 19(4): 465–474.
Chen, X., Fan, Y. & Patton, A. (2004). Simple tests for models of dependence between multiple financial time series with applications to u.s. equity returns and exchange rates, Technical report, Financial Markets Group, International Asset Management.
Dobric, J. & Schmidt, F. (2007). A goodness of fit test for copulas based on rosenblatt’s transformation, Computational Statistics and Data Analysis 51(9): 4633–4642.
Doornik, J. & Hansen, H. (1994). An omnibus test for univariate and multivariate normality, Technical report, University of Oxford, University of Copenhagen.
Fermanian, J.-D. (2005). Goodness-of-fit tests for copulas, Journal of Multivariate Analysis 95: 119–152.
Frerichs, H. & Loeffler, G. (2003). Evaluating credit risk models using loss density forecasts, Journal of Risk 5(4): 1–23.
Frey, R. & McNeil, A. (2003). Dependent defaults in models of portfolio credit risk, Journal of Risk 6(1): 59–92.
Genest, C., Quessy, J. & Rémillard, B. (2006). Goodness-of-fit procedures for copula models based on the probability integral transformation, Scandinavian Journal of Statistics 33: 337–366.
Hamerle, A. & Plank, K. (2009). A note on the Berkowitz test with discrete distributions, Journal of Risk Model Validation 3(2): 3–10.
Hamerle, A. & Roesch, D. (2005). Misspecified copulas in credit risk models: How good is gaussian?, Journal of Risk 8(1): 41–58.
Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 3: 73–84.
Lopez, J. & Saidenberg, M. (2000). Evaluating credit risk models, Journal of Banking and Finance 24: 151–165.
Marshall, A. & Olkin, I. (1988). Families of multivariate distributions, Journal of the American Statistical Association 83: 834–841.
McNeil, A., Frey, R. & Embrechts, P. (2005). Quantitative Risk Management.Concepts, Techniques, Tools, Princeton University Press.
Moosbrucker, T. (2006). Copulas from infinitely divisible distributions - applications to credit value at risk, Technical report, University of Cologne.
Nikoloulopoulos, A. & Karlis, D. (2008). Copula model evaluation based on parametric bootstrap, Computational Statistics and Data Analysis p. in press.
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Hamerle, A., Plank, K. (2010). Copula Choice with Factor Credit Portfolio Models. In: Kneib, T., Tutz, G. (eds) Statistical Modelling and Regression Structures. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2413-1_17
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DOI: https://doi.org/10.1007/978-3-7908-2413-1_17
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