A Latent Variable Credit Risk Model Comprising Nonlinear Dependencies in a Sector Framework with a Stochastically Dependent Loss Given Default

38 Pages Posted: 6 Jan 2018

See all articles by Jakob Maciag

Jakob Maciag

zeb.rolfes.schierenbeck.associates GmbH

Matthias Löderbusch

University of Muenster - Finance Center Muenster

Date Written: July 31, 2017

Abstract

In this paper, we propose a latent variable credit risk model for large loan portfolios. It employs the concept of nested Archimedean copulas to account for both a sector-type dependence structure and a copula-dependent stochastic loss given default (LGD). Using this framework, we conduct an extensive Monte Carlo simulation study and analyze the impact of various nested Archimedean and elliptical copulas, the sector-type dependence structure and a stochastically dependent LGD on portfolio tail risk. Further, we examine the effect of calibrating the different copulas, either on default correlations or on a measure of global coherence. We find that employing non-Gaussian copulas in a sector-type portfolio model can be accompanied by a significant increase in terms of measured riskiness. Although the value-at-risk (VaR) measurements partially converge when the copulas are calibrated on default correlations, substantial differences between the (nested) copulas remain. We compare homogeneous and heterogeneous sector-type portfolios and find the latter yielding slightly smaller VaRs. Moreover, the restrictions of the nested copula model can attenuate the impact of heavy-tailed copulas on portfolio tail risk. By contrast, for most of the (nested) copulas, a copula-dependent stochastic LGD increases the measured riskiness of the portfolio’s loss rate distribution remarkably.

Keywords: Copula, Nested Copula, Sector-Type Credit Portfolio Models, Credit Risk, Stochastic Loss Given Default (LGD)

Suggested Citation

Maciag, Jakob and Löderbusch, Matthias, A Latent Variable Credit Risk Model Comprising Nonlinear Dependencies in a Sector Framework with a Stochastically Dependent Loss Given Default (July 31, 2017). Journal of Credit Risk, Vol. 13, No. 4, 2017. Available at SSRN: https://ssrn.com/abstract=3096040

Jakob Maciag

zeb.rolfes.schierenbeck.associates GmbH

Germany

Matthias Löderbusch (Contact Author)

University of Muenster - Finance Center Muenster ( email )

Schlossplatz 2
Muenster
Germany

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