Asset correlation of retail loans in the context of the new Basel Capital Accord

Siarka, Paweł

doi:10.21314/jcr.2014.178

Cited by 7 publications

(2 citation statements)

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“…The last key element is the borrower asset correlation. Its value significantly affects the unexpected losses, which impacts the capital requirements described in detail by Zeng [2] and Siarka [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling Recoveries of US Leading Banks Based on Publicly Disclosed Data

Siarka

2021

Mathematics

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The credit risk management process is a critical element that allows financial institutions to withstand economic downturns. Unlike the methods regarding the probability of default, which have been deeply addressed after the financial crisis in 2008, recovery rate models still need further development. As there are no industry standards, leading banks are modeling recovery rates using internal models developed with different assumptions. Therefore, the outcomes are often incomparable and may lead to confusion. The author presents the concept of a unified recovery rate analysis for US banks. He uses data derived from FR Y-9C reports disclosed by the Federal Reserve Bank of Chicago. Based on the historical recoveries and credit portfolio book values, the author examines the distribution function of recoveries. The research refers to a credit card portfolio and covers nine leading US banks. The author leveraged Vasicek’s one-factor model with the asset correlation parameter and implemented it for recovery rate analysis. This experiment revealed that the estimated latent correlation ranges from 0.2% to 1.5% within the examined portfolios. They are large enough to impact the recovery rate volatility and cannot be treated as negligible. It was shown that the presented method could be applied under US Comprehensive Capital Analysis and Review exercise.

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Section: Introductionmentioning

confidence: 99%

Modeling Recoveries of US Leading Banks Based on Publicly Disclosed Data

Siarka

2021

Mathematics

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“…IntheliteraturetherearemanystudiesreferringtotheproblemofPDmodel qualityassessment[Jackson,Perraudin2000]aswellastheproblemofPDmodel implementation. Kupiec[1995]presentedoneoftheleadingapproachesbasedonthe binomialtest.However,hepointedoutthatthisparticularmethodmaynotbethe mostefficientduetostringentassumptionsmadebyitsauthor.Coppens [Coppens, Gonzalez,Winkler2000]referredtotheimpactofborrower'sassetcorrelationon backtestinganalysisoutcomes.Heunderlinedthatdependenciesbetweenborrower defaultsmaysignificantlyaffectthelossdistributionfunction.Hence,conclusions drawnbasedonbinomialstatisticaltestsmaynotbevalidanymore.TascheandPluto [2005]alsoaddressedthisproblem.Anoverviewofthestatisticalmethodswhich can be leveraged for asset correlation estimation was presented by Siarka [2014]. Tasche [2003]presentedageneralformofPDmodelvalidationusingthetrafficlight approach.Similarly, Blochwitz[Blochwitzetal.2005] Thepaperstartswithanintroduction.Inthenextparttheauthorpresentsthe one-factormodelrecommendedbytheBaselCommittee.Themodelisshownonthe basisofaminimumcapitalrequirementscalculation.TheroleofthePDparameter in the process of unexpected losses estimation was also emphasized.…”

Section: Introductionmentioning

confidence: 99%

Backtesting analysis. How to assess the quality of PD models in a retail banking

Siarka¹

2019

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

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Thepaperreferstotheprobabilityofdefaultmodelvalidationprocedureinretail banking.Theauthorpresentstheideaofbacktestinganalysisfocusingonsensitivityanalysis of capital requirements under stress scenarios. The paper addresses statistical methods which can be applied in credit risk management under the backtesting exercise in retail banking.Theadvantagesanddrawbacksofspecificapproachesarediscussed.Furthermore, theoutcomesoftheempiricalimplementationofselectedmethodsarepresented.Theauthor considerstheimpactofpositiveassetcorrelationonvariousvalidationapproaches,whereno correlationisassumed,andprovesthatthezero-correlationassumptionsmayresultinamore prudentapproach.Thisfindingwasconfirmedbytheempiricalanalysisperformedforretail portfolios. The research concerned PD parameters calculated for car and mortgage loans. The backtesting results revealed that PD forecasts created for mortgage portfolios underestimatedcreditriskduringthecrisisperiodwhichstartedin2008.However,carloan portfoliocreditriskpredictionsappearedtoberobust.

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Testing the predictive power: A comparative study of current default probability validation tests

Blümke¹

2022

Expert Systems with Applications

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Asset correlation of retail loans in the context of the new Basel Capital Accord

Cited by 7 publications

References 0 publications

Modeling Recoveries of US Leading Banks Based on Publicly Disclosed Data

Modeling Recoveries of US Leading Banks Based on Publicly Disclosed Data

Backtesting analysis. How to assess the quality of PD models in a retail banking

Testing the predictive power: A comparative study of current default probability validation tests

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