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Proposal on ELBE and LGD in-default: tackling capital requirements after the financial crisis

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  • González, Marta Ramos
  • Ureña, Antonio Partal
  • Fernández-Aguado, Pilar Gómez

Abstract

Following the financial crisis, the share of non-performing loans has significantly increased, while the regulatory guidelines on the Internal-Ratings Based (IRB) approach for capital adequacy calculation related to defaulted exposures remains too general. As a result, the high-risk nature of these portfolios is clearly in danger of being managed in a heterogeneous and inappropriate manner by those financial institutions permitted to use the IRB system, with the consequent undue variability of Risk-Weighted Assets (RWA). This paper presents a proposal to construct Advanced IRB models for defaulted exposures, in line with current regulations, that preserve the risk sensitivity of capital requirements. To do so, both parameters Expected Loss Best Estimate (ELBE) and Loss Given Default (LGD) in-default are obtained, backed by an innovative indicator (Mixed Adjustment Indicator) that is introduced to ensure an appropriate estimation of expected and unexpected losses. The methodology presented has low complexity and is easily applied to the databases commonly used at these institutions, as illustrated by two examples. JEL Classification: C51, G21, G28, G32

Suggested Citation

  • González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2018. "Proposal on ELBE and LGD in-default: tackling capital requirements after the financial crisis," Working Paper Series 2165, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20182165
    Note: 3037891
    as

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    References listed on IDEAS

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    1. repec:bla:ecnote:v:33:y:2004:i:2:p:183-208 is not listed on IDEAS
    2. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    3. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2016. "What drives the time to resolution of defaulted bank loans?," Finance Research Letters, Elsevier, vol. 18(C), pages 7-31.
    4. repec:bla:jfinan:v:44:y:1989:i:4:p:909-22 is not listed on IDEAS
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    7. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    8. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    9. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
    10. Altman, Edward I. & Suggitt, Heather J., 2000. "Default rates in the syndicated bank loan market: A mortality analysis," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 229-253, January.
    11. Fenech, Jean Pierre & Yap, Ying Kai & Shafik, Salwa, 2016. "Modelling the recovery outcomes for defaulted loans: A survival analysis approach," Economics Letters, Elsevier, vol. 145(C), pages 79-82.
    12. Düllmann, Klaus & Trapp, Monika, 2004. "Systematic Risk in Recovery Rates: An Empirical Analysis of US Corporate Credit Exposures," Discussion Paper Series 2: Banking and Financial Studies 2004,02, Deutsche Bundesbank.
    13. Dermine, J. & Neto de Carvalho, C., 2008. "Bank loan-loss provisioning, central bank rules vs. estimation: The case of Portugal," Journal of Financial Stability, Elsevier, vol. 4(1), pages 1-22, April.
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    More about this item

    Keywords

    banking regulation; credit risk; defaulted exposures;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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