A Two-Factor Model for PD and LGD Correlation

Witzany, Jiří

doi:10.2139/ssrn.1476305

Cited by 18 publications

(12 citation statements)

References 11 publications

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“…There is a large literature on modelling PD-LGD correlation (Frye 2000); (Pykhtin 2003); (Miu and Ozdemir 2006); (Kupiec 2008); (Sen 2008); (Witzany 2011); (de Wit 2016); (Eckert et al 2016); and others listed in (Frye and Jacobs 2012), but there is a much smaller literature on using IS to estimate large deviation probabilities in such models. To the best of our knowledge only (Deng et al 2012) and (Jeon et al 2017) have developed algorithms that allow for PD-LGD correlation (the former paper considers a dynamic intensity-based framework, the latter considers a static model with asymmetric and heavy-tailed risk factors).…”

Section: Problem Formulation and Related Literaturementioning

confidence: 99%

Importance Sampling in the Presence of PD-LGD Correlation

Metzler

Scott

2020

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This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans can be predicted with certainty, i.e., that loss given default (LGD) is non-random. In practice, however, LGD is impossible to predict and tends to be positively correlated with the default rate and the latter phenomenon is typically referred to as PD-LGD correlation (here PD refers to probability of default, which is often used synonymously with default rate). There is a large literature on modelling stochastic LGD and PD-LGD correlation, but there is a dearth of literature on using importance sampling to estimate large deviation probabilities in those models. Numerical evidence indicates that the proposed algorithms are extremely effective at reducing the computational burden associated with obtaining accurate estimates of large deviation probabilities across a wide variety of PD-LGD correlation models that have been proposed in the literature.

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Section: Problem Formulation and Related Literaturementioning

confidence: 99%

Importance Sampling in the Presence of PD-LGD Correlation

Metzler

Scott

2020

Risks

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“…However, even in this case the series of systematic factors might be autocorrelated and a more general specification should be considered (see e.g. Witzany, 2011).…”

Section: The Single-factor Default Modelmentioning

confidence: 99%

Stressing of Migration Matrices for IFRS 9 and ICAAP Calculations

Witzany

2020

SSRN Journal

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Its aim is to provide a platform for fast dissemination, discussion, and feedback on preliminary research results before submission to regular refereed journals. The papers are peer-reviewed, but are not edited or formatted by the editors. Disclaimer:The views expressed in documents served by this site do not reflect the views of the Faculty of Finance and Accounting or any other University of Economics Faculties and Departments. They are the sole property of the respective authors.

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“…The correlation between the two idiosyncratic factors is w. In practice, this approach is used by the Moody model (Meng et al, 2010). Another extension of the KMV model can be found in Witzany (2011). In this model LGD is assumed to be driven by a specific factor different from the one driving defaults and by two systemic factors, one common to the defaults and the other specific to LGD.…”

Section: Existing Models With Random Lgdmentioning

confidence: 99%

Dynamic Multi-Factor Credit Risk Model with Fat-Tailed Factors

Šmíd

Gapko

2010

SSRN Journal

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We introduce an improved multi-factor credit risk model describing simultaneously the default rate and the loss given default. Our methodology is based on the KMV model, which we generalize in three ways. First, we add a model for loss given default (LGD), second, we bring dynamics to the model, and third, we allow non-normal distributions of risk factors. Both the defaults and the LGD are driven by a common factor and an individual factor; the individual factors are mutually independent, but we allow any form of dependence of the common factors. We test our model on a nationwide portfolio of US mortgage delinquencies, modeling the dependence of the common factor by a VECM model, and compare our results with the current regulatory framework, which is described in the Basel II Accord. * Support from the Czech Science Foundation under grants 402/09/H045 and 402/09/0965 and from Charles University under grant GAUK 46108 is gratefully acknowledged. The authors are also grateful to Jan Voříšek for valuable comments and the idea of using the VECM model for the description of the factors. 1 PD and LGD are usually referred to as risk factors; however, in this paper we call them "indicators" in order to verbally distinguish between these main quantities and the factors that drive them.

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A Two-Factor Model for PD and LGD Correlation

Cited by 18 publications

References 11 publications

Importance Sampling in the Presence of PD-LGD Correlation

Importance Sampling in the Presence of PD-LGD Correlation

Stressing of Migration Matrices for IFRS 9 and ICAAP Calculations

Dynamic Multi-Factor Credit Risk Model with Fat-Tailed Factors

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