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VALIDATION of LOSS GIVEN DEFAULT for CORPORATE Miloš Vujnović* Jubmes Banka, Serbia Nebojša Nikolić Jubmes Banka, Serbia Anja Vujnović Jubmes Banka, Serbia

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VALIDATION of LOSS GIVEN DEFAULT for CORPORATE Miloš Vujnović* Jubmes Banka, Serbia Nebojša Nikolić Jubmes Banka, Serbia Anja Vujnović Jubmes Banka, Serbia Original Scientific Paper doi:10.5937/jaes14-11752 Paper number: 14(2016)4, 403, 465 - 476 VALIDATION OF LOSS GIVEN DEFAULT FOR CORPORATE Miloš Vujnović* Jubmes Banka, Serbia Nebojša Nikolić Jubmes Banka, Serbia Anja Vujnović Jubmes Banka, Serbia This paper presents an contemporary approach for development and validation of Loss given default (LGD) in accordance with the Basel Accords standards. The modeling data set has been based on data on recoveries of outstanding debts from corporate entities in Republic of Serbia that defaulted. The aim of the paper is to develop a LGD model capable of confirming the validity of historically ob- served LGD estimates on the sample of corporate entities that defualted. The modelling approach in this research is based on average LGD without time or exposure weightening. The probability density function of realized empirical LGDs has been created by beta distribution usage. The valida- tion process on proposed LGD model has been performed by throughout testing of: cumulative LGD accuracy ratio, mean square error calculation and regression analysis. On the basis of obtained results, the possibilities of application of the developed LGD model are proposed and discussed. Key words: Loss Given Default; LGD; Model, Portfolio; Serbia INTRODUCTION and promotes the use of internal models for cal- culating credit risk parameters and capital calcu- In the context of credit risk modeling, the term lation [04]. Basel II framework emphasizes three “validation” includes the set of processes and approaches to quantifying LGD: workout LGD, activities that contribute to the standpoint that market LGD and implied market LGD. The vis- risk components adequately characterize rel- ibility and attractiveness of LGD has also been evant risk aspects, the risk components being recognized in new IFRS 9 standard. The new the probability of default (PD), the loss given de- IFRS 9 standard extends the usage of LGD not fault (LGD) and the exposure at default (EAD). only for calculation of risk weighted assets, as The validation framework includes all aspects of currently under Basel Capital Accord IRB ap- validation which are, in this context, defined by proach, but for calculation of loan loss provision general principles of validation published by the and allowances. Basel Committee on Banking Supervision [03]. Clear definition of default is the prerequisite for LGD represents the credit risk parameter that the LGD estimation. Another basic prerequisite plays an important role in contemporary banking is the definition of LGD. Depending on definition risk management practice. It contributes as the of the time of default, LGD calculation may of- key risk parameter in regulatory capital calcula- fer different results. If the model is to be used tion according to IRB approach [4], as well as for for capital calculation in accordance with Basel banks’ internal risk management process. Pri- II standards, it is necessary to use the regulatory mary reason for such incentive is the permission definition of default [04], according to which LGD for the banks to use the real LGDs from experi- is based on economic loss, where the bank must ence instead of fixed regulatory LGDs. The aim estimate LGD for each placement in such a way of LGD estimate is to accurately and efficiently that it reflects the recession conditions, which is quantify the level of recovery risk inherited within necessary in order to include all relevant risks. a defaulted exposure. Contemporary risk man- LGD estimates must be based on historical re- agement practice and regulation emphasizes covery rates and, where applicable, they should *Jubmes Banka AD, Bulevar Zorana Đinđića 121, 11000 Belgrade, Serbia; 465 [email protected] Miloš Vujnović - Validation of loss given default for corporate be based on estimated market value of a col- business cycle periods provide better fit than the lateral. data from total available time span [18]. Basel II defines the validation as one of the re- The paper of Qi and Zhao [14] compared six quirements regarding LGD so that estimation of modeling methods for LGD and found that non- the same is acceptable for definition of the regula- parametric methods (regression tree and neural tory capital. Banks must have a sustainable sys- network) perform better than parametric meth- tem for validation of accuracy and consistency ods both in and out of sample when over-fitting of the rating system, processes and all relevant is properly controlled. The Farinelli and Shkol- components. Comparison between realized and nikov [8] study pointed out that LGD follow beta estimated LGD must be performed regularly (at distributions with means estimated from histori- least once a year) in order to demonstrate that cal data. The shapes of the beta distributions the realized LGD is within the expected value vary across firms in such a way that the density framework. function is concave if the corresponding credit Although recent research led to advanced back- instrument is backed by a collateral and convex testing methods for PD models, the literature on otherwise. similar backtesting methods for LGD models is The global financial crisis highlighted the fact that much scarcer. default and recovery rates of multiple borrowers In this sense, the framework for backtesting of generally deteriorate jointly during economic LGD model was offered by Loterman, Debruyne, downturns. The vast majority of the literature, as Vanden Branden, Van Gestel, Mues [13]. Current well as many industry credit-portfolio risk mod- LGD performance evaluation practices found in els, ignore this and analyze default probabilities the literature have so far been usually limited to and recoveries in the event of default separately. comparing internal LGD predictions and realized The paper of Bade, Rösch and Scheule [05] is LGD observations using error-based metrics, the first of its kind to assess the performance al- correlation-based metrics or even classification- ternatives that incorporate the dependence be- based metrics [12]. Most of the LGD studies fo- tween probabilities of default and recovery rates. cus on investigating the importance of various In it four banks using different estimation proce- factors that affect LGD, for example, contract dures are compared. characteristics, borrower characteristics, indus- Further researches of LGD are discussed and try conditions, and macroeconomic conditions. implemented by Antăo and Lacerda [02], Thom- Very few studies of LGD explore the alternative as, Matuszyk and Moore [17], Jankowitsch, Pul- modeling methodologies [01]. lirsch and Vezˇa [11], Calabrese [06], Jacobs Jr. The research of Gurtler and Hibbeln [9] theoreti- and Karagozoglu [10]. cally analyze problems arising when forecast- LOSS GIVEN DEFAULT ESTIMATION ing LGDs of bank loans that lead to inconsistent METHODOLOGY AND EXPERIMENTAL DESIGN estimates and a low predictive power. The re- search present several improvements for LGD LGD estimation is the first step in the process of estimates, considering length-biased sampling, validation. However, LGD estimation may be a different loan characteristics depending on the challenge mainly due to limited data availability. type of default end, and different information sets Basel II emphasizes the need of banks to de- according to the default status. It shows how the velop and apply internal credit risk models and modeling data could be restricted in order to ob- therefore quantitative models of LGD estima- tain unbiased LGD estimates. tion represent the basis for application of IRB The predictive power of any LGD model depends approach for corporate entities. The banks are on proper choice (and availability) of the model required to enable LGD estimation based on the input parameters obtained from obligor’s infor- group of borrowers with similar characteristics. mation. However, LGD estimation may be a challenge For a given input data set, the model calibra- mainly due to limited data availability. tion quality depends mainly on the proper choice In view of the fact that LGD is one of the basic (and availability) of explanatory variables (model inputs of the credit risk model, the primary prob- factors), but not on the model used for fitting. lem may occur in the case of small number of Calibration of LGD models using distressed defaults. 466 Journal of Applied Engineering Science 14(2016)4, 403 Miloš Vujnović - Validation of loss given default for corporate Basel II framework published in relation to the calculated for the purpose of its use as a basic validation principles [03] is considered to be credit risk parameter. As so, following implemen- sufficiently flexible so that even portfolios with tation possibilities are considered: calculation a small number of default incidents are accept- of level of depreciation of exposure at a collec- able for application under the IRB systems. As in tive level in accordance with IAS requirements, the case of other portfolios, they must fulfill mini- determination of methodological approach for mum criteria established by the Basel framework placement price calculation (interest or discount that include requirements for a sensible, precise rate) based on adequate and accurate estima- and consistent quantitative risk estimations. The tion of margin for undertaken risk, calculation choice of tools and techniques will considerably of minimal capital requirements for credit risk depend on the situation of the individual bank by application of IRB approach, fulfilment of
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