Abstract
AbstractBased on a unique data set of 909 defaulted retail and commercial (self-employed and SMEs) credit customers in Germany, whose original loans were made by 123 different banks, our article confirms a significant positive influence of collateral, and of amicable agreements between the debtor and the bank (redemption), on the recovery rate [1 − loss given default (LGD)]. In a further analysis of collateral, systematic biases between the realized market price and the expected market values of real estate are revealed, even though the appraisal reports should have already considered all factors influencing the value. Using valuations that were adjusted for these recognized biases, we can increase the explanatory power of the underlying models. Moreover, we compare these models to models that apply, as is common practice in the banking industry, flat haircuts to collateral values and show the superior performance of our proposed approach.
Highlights
The probability of default (PD) and the recovery rate [1 - loss given default (LGD)] are the key parameters for risk-adjusted pricing of loans in the context of Basel II
Based on a unique data set of 909 defaulted retail and commercial credit customers in Germany, whose original loans were made by 123 different banks, our article confirms a significant positive influence of collateral, and of amicable agreements between the debtor and the bank, on the recovery rate [1 - loss given default (LGD)]
For internal risk management purposes, the recovery rate, the PD, the exposure at default (EAD), and their correlations are included in the calculation of expected and unexpected losses of the credit portfolio, and thereby influence the calculation of the value at risk (VaR) as well as credit terms
Summary
Our study investigates the recovery rate [1 - loss given default (LGD)] of bank loans, a key parameter in the context of Basel II, for retail and commercial (selfemployed and SMEs) customers in Germany. The recovery rate relates the proceeds and costs of realization to the outstanding amount at the time of default [exposure at default (EAD)]. It is included in the calculation of expected and unexpected losses in the credit portfolio and thereby influences the calculation of the value at risk (VaR) as well as (future) credit terms. Adjusting the collateralization values for these recognized systematic biases significantly improves the explanatory power of our model for recovery rates. While the results may hold for a greater universe, they are relevant for savings banks and cooperative banks because of the focus of these banks on the customer segment analyzed here
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