Exposure at default modeling – A theoretical and empirical assessment of estimation approaches and parameter choice

Marc Gürtler, Martin Thomas Hibbeln and Piet Usselmann

Journal of Banking & Finance, 2018, vol. 91, issue C, 176-188

Abstract: Estimating the credit risk parameter exposure at default is important for banks from an internal risk management and a regulatory perspective. Several approaches are common in the literature and in practice. We theoretically and empirically analyze how the exposure at default should be modeled to obtain accurate estimates of the expected loss. Our empirical analysis is based on a large and unique dataset from a retail portfolio of a European bank. We demonstrate that some approaches can lead to substantially biased estimates of the expected loss and show that the generalized cohort approach is advantageous. Moreover, using in- and out-of-sample analyses, we empirically demonstrate that using the credit conversion factor is preferable to the loan equivalent factor, exposure at default factor, and direct exposure at default estimation to achieve high estimation accuracy.

Keywords: Credit risk; Checking accounts; Exposure at default; Credit conversion factor; Probability of default (search for similar items in EconPapers)
JEL-codes: G21 G28 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:91:y:2018:i:c:p:176-188

DOI: 10.1016/j.jbankfin.2017.03.004

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