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The bank regulation embodied in the Basel II Accord has opened-up a new era in estimating recovery rates or complementary loss given default in retail lending credit evaluation process. In this paper we investigate the properties of survival analysis models applied for recovery rates in order to predict loss given default for retail lending. We compare the results to standard techniques such as linear and logistic regressions and discuss the pros and cons of the respective methods. The study is performed on a real dataset of a major Czech bank.
The paper analyzes a two-factor credit risk model allowing to capture default and recovery rate variation, their mutual correlation, and dependence on various explanatory variables. At the same time, it allows computing analytically the unexpected credit loss. We propose and empirically implement estimation of the model based on aggregate and exposure level Moody’s default and recovery data. The results confirm existence of significantly positive default and recovery rate correlation. We empirically compare the unexpected loss estimates based on the reduced two-factor model with Monte Carlo simulation results, and with the current regulatory formula outputs. The results show a very good performance of the proposed analytical formula which could feasibly replace the current regulatory formula.