Survival Analysis in LGD Modeling

Witzany, Jiří; Rychnovský, Michael; Charamza, Pavel

doi:10.18267/j.efaj.12

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…In contrast to our approach, this does not take the time spent in default into account, but the amount of repayments until the final value of repaid debt is known. Examples for this approach can be found in Dermine and Neto de Carvalho (2006), Witzany, Rychnovsky and Charamza (2012) and Zhang and Thomas (2012). The latter additionally analyse the improvement in estimation when segmenting LGDs with decision trees before estimation, hereby, constructing a mixture of the LGD distribution.…”

Section: Find Indications For Non-linear Relationships Betweenmentioning

confidence: 99%

Time Matters: How Default Resolution Times Impact Final Loss Rates

Betz

Kellner

Rösch

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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The authors thank Global Credit Data (GCD) for granting access to their database and the participants of the NBER (time series) 2018 conference, the CREDIT 2018 conference, and the CEQURA 2018 conference for fruitful discussions and comments on an earlier version of this paper. The authors are particularly grateful to Prof. Alain Monfort for a thorough and valuable discussion of the paper during the CREDIT 2018 conference.

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Section: Find Indications For Non-linear Relationships Betweenmentioning

confidence: 99%

Time Matters: How Default Resolution Times Impact Final Loss Rates

Betz

Kellner

Rösch

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…The workout LGD is a cashflow-oriented explicit method and is typically applied when estimating LGD for unsecured retail products (Witzany, 2012). It has also been used for secured portfolios, where collateral information is used as explanatory variables for the LGD dependent variable.…”

Section: Models For Estimatingmentioning

confidence: 99%

“…The workout LGD is equal to one minus the recovery rate, where the recovery rate can be calculated as the sum of all future recoveries, discounted to the default point and then expressed as a fraction of the exposure at default. Zhang & Thomas (2012), Schmidt (2006) and Witzany (2012) respectively make use of a linear regression, run-off triangle and Cox proportional hazards regression to model the recovery rate directly. Focussing directly on the loss amount, Tong, Mues & Thomas (2013) make use of a zero adjusted gamma model.…”

Section: Models For Estimatingmentioning

confidence: 99%

“…Because the focus of this paper is on the clarification of the MoC concept as opposed to a detailed exposition of LGD modelling techniques, the interested reader is referred to the papers by Lotheram, Brown, Martens, Mues & Baesens (2012), Qi & Zhao (2011), Bellotti & Crook (2012, Leow & Mues (2012) and Witzany (2012) for a comparison of LGD modelling techniques. However, the brief overview presented here does illustrate that LGD modelling may involve various approaches, some of which are quite complicated.…”

Section: Models For Estimatingmentioning

confidence: 99%

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A Critical Review Of The Basel Margin Of Conservatism Requirement In A Retail Credit Context

Jongh

Verster

Reynolds

et al. 2017

IBER

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The Basel II accord (2006) includes guidelines to financial institutions for the estimation of regulatory capital (RC) for retail credit risk. Under the advanced Internal Ratings Based (IRB) approach, the formula suggested for calculating RC is based on the Asymptotic Risk Factor (ASRF) model, which assumes that a borrower will default if the value of its assets were to fall below the value of its debts. The primary inputs needed in this formula are estimates of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Banks for whom usage of the advanced IRB approach have been approved usually obtain these estimates from complex models developed in-house. Basel II recognises that estimates of PDs, LGDs, and EADs are likely to involve unpredictable errors, and then states that, in order to avoid over-optimism, a bank must add to its estimates a margin of conservatism (MoC) that is related to the likely range of errors. Basel II also requires several other measures of conservatism that have to be incorporated. These conservatism requirements lead to confusion among banks and regulators as to what exactly is required as far as a margin of conservatism is concerned. In this paper, we discuss the ASRF model and its shortcomings, as well as Basel II conservatism requirements. We study the MoC concept and review possible approaches for its implementation. Our overall objective is to highlight certain issues regarding shortcomings inherent to a pervasively used model to bank practitioners and regulators and to potentially offer a less confusing interpretation of the MoC concept.

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“…Survival analysis is a common statistical method from the medical and healthcare sectors (see Marubini & Valsecchi, 1995or Collet, 2003, but has also found widespread use in credit risk (Witzany et al, 2012or Witzany, 2017. One of the first uses of survival analysis in banking can be attributed to Narain (1992).…”

Section: Introductionmentioning

confidence: 99%

Recovery Process Optimization Using Survival Regression

Witzany

Kozina²

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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Survival Analysis in LGD Modeling

Cited by 16 publications

References 9 publications

Time Matters: How Default Resolution Times Impact Final Loss Rates

Time Matters: How Default Resolution Times Impact Final Loss Rates

A Critical Review Of The Basel Margin Of Conservatism Requirement In A Retail Credit Context

Recovery Process Optimization Using Survival Regression

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