Modelling Recovery Rate for Incomplete Defaults Using Time Varying Predictors

Wojciech Starosta ()
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Wojciech Starosta: University of Lodz, Poland

Central European Journal of Economic Modelling and Econometrics, 2020, vol. 12, issue 2, 195-225

Abstract: The Internal Rating Based (IRB) approach requires that financial institutions estimate the Loss Given Default (LGD) parameter not only based on closed defaults but also considering partial recoveries from incomplete workouts. This is one of the key issues in preparing bias-free samples, as there is a need to estimate the remaining part of the recovery for incomplete defaults before including them in the modeling process. In this paper, a new approach is proposed, where parametric and non-parametric methods are presented to estimate the remaining part of the recovery for incomplete defaults, in predefined intervals concerning sample selection bias. Additionally it is shown that recoveries are driven by different set of characteristics when default is aging. As an example, a study of major Polish bank is presented, where regression tree outperforms other methods in the secured products segment, and fractional regression provides the best results for non-secured ones.

Keywords: LGD; workout approach; incomplete defaults; partial recovery rate (search for similar items in EconPapers)
JEL-codes: C51 G32 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:psc:journl:v:12:y:2020:i:2:p:195-225

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