Modelling Bank Loan LGD of Corporate and SME Segments A Case Study
|Autor:|| Radovan Chalupka Ph.D., |
|Typ:||IES Working Papers|
|ISSN / ISBN:|
|Publikováno v:||IES Working Papers 27/2008|
|Klíčová slova:||credit risk, bank loan, loss given default, LGD, recovery rate, fractional responses, ordinal regression, quasi-maximum likelihood estimator|
|JEL kódy:||G21, G28|
|Citace:||Chalupka, R., Kopecsni, J. (2008). “ Modelling Bank Loan LGD of Corporate and SME Segments: A Case Study ” IES Working Paper 27/2008. IES FSV. Charles University.|
|Granty:||GACR 402/05/H510 Ekonomická teorie politických trhů GAUK 131707 - Modelling Loss Given Default for SME and corporate segment: the case of Czech banking system|
|Abstrakt:||The aim of this paper is to propose a methodology to estimate loss given default (LGD) and apply it to a set of micro-data of loans to SME and corporations of an anonymous commercial bank from Central Europe. LGD estimates are important inputs in the pricing of credit risk and the measurement of bank profitability and solvency. Basel II Advance IRB Approach requires internally estimates of LGD to calculate risk-weighted assets and to estimate expected loss.
We analyse the recovery rate dynamically over time and identify the efficient recovery period of a workout department. Moreover, we focus on the appropriate choice of a discount factor by introducing risk premium based on a risk level of collaterals. We apply statistical methods to estimate LGD and test empirically its determinants.
Particularly, we analyse generalised linear models using symmetric logit and asymmetric log-log link functions for ordinal responses as well as for fractional responses. For fractional responses we employ two alternatives, a beta inflated distribution and a quasi-maximum likelihood estimator.
We find out that the main drivers of LGD are a relative value of collateral, a loan size as well as a year of the loan origination. Different models provided similar results. As for the different links in more complex models, log-log models in some cases perform better, implying an asymmetric response of the dependent variable.
WP 2008_27_Chalupka, Kopecsni