IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i9p1462-d1645779.html

Inferred Loss Rate as a Credit Risk Measure in the Bulgarian Banking System

Author

Listed:
  • Vilislav Boutchaktchiev

    (Faculty of Applied Informatics and Statistics, University of National and World Economy, 1700 Sofia, Bulgaria
    Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria)

Abstract

The loss rate of a bank’s portfolio traditionally measures what portion of the exposure is lost in the case of a default. To overcome the difficulties involved in its computation due to, e.g., the lack of private data, one can utilize an inferred loss rate (ILR). In the existing literature, it has been demonstrated that this indicator has sufficiently close properties to the actual loss rate to facilitate capital adequacy analysis. The current study provides complete mathematical proof of an earlier-stated conjecture, that ILR can be instrumental in identifying a conservative upper bound of the capital adequacy requirement of a bank credit portfolio, using the law of large numbers and other techniques from measure-theory-based probability. The assumptions required in this proof are less restrictive, reflecting a more realistic view. In the current study, additional empirical evidence of the usefulness of the indicator is provided, using publicly available data from the Bulgarian National Bank. Despite the definite conservativeness of the capital buffer implied from the analysis of ILR, the empirical analysis suggests that it is still within the regulatory limits. Analyzing ILR together with the Inferred Rate of Default, we conclude that the indicator provides signals about a bank portfolio’s credit risk that are relevant, timely, and adequately inexpensive.

Suggested Citation

  • Vilislav Boutchaktchiev, 2025. "Inferred Loss Rate as a Credit Risk Measure in the Bulgarian Banking System," Mathematics, MDPI, vol. 13(9), pages 1-18, April.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1462-:d:1645779
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/9/1462/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/9/1462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gourieroux, C. & Laurent, J. P. & Scaillet, O., 2000. "Sensitivity analysis of Values at Risk," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 225-245, November.
    2. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    3. Ibragimov, Rustam & Walden, Johan, 2007. "The limits of diversification when losses may be large," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2551-2569, August.
    4. Vitanov, Nikolay K. & Sakai, Kenshi & Jordanov, Ivan P. & Managi, Shunsuke & Demura, Katsuhiko, 2007. "Analysis of a Japan government intervention on the domestic agriculture market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 330-335.
    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. Frey, Rudiger & McNeil, Alexander J., 2002. "VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1317-1334, July.
    7. Paul Embrechts & Giovanni Puccetti & Ludger Rüschendorf & Ruodu Wang & Antonela Beleraj, 2014. "An Academic Response to Basel 3.5," Risks, MDPI, vol. 2(1), pages 1-24, February.
    8. Dirk Tasche, 2001. "Conditional Expectation as Quantile Derivative," Papers math/0104190, arXiv.org.
    9. Paul Embrechts & Bin Wang & Ruodu Wang, 2015. "Aggregation-robustness and model uncertainty of regulatory risk measures," Finance and Stochastics, Springer, vol. 19(4), pages 763-790, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Venelin Todorov & Petar Zhivkov, 2025. "Efficient Evaluation of Sobol’ Sensitivity Indices via Polynomial Lattice Rules and Modified Sobol’ Sequences," Mathematics, MDPI, vol. 13(21), pages 1-20, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rosen, Dan & Saunders, David, 2009. "Analytical methods for hedging systematic credit risk with linear factor portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 37-52, January.
    2. Gordy, Michael B. & Marrone, James, 2012. "Granularity adjustment for mark-to-market credit risk models," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1896-1910.
    3. Arturo Cortés Aguilar, 2011. "Estimación del residual de un bono respaldado por hipotecas mediante un modelo de riesgo crédito: una comparación de resultados de la teoría de cópulas y el modelo IRB de Basilea II en datos del mercado hipotecario mexicano," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 5(1), pages 50-64.
    4. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Ruodu Wang, 2017. "Risk bounds for factor models," Finance and Stochastics, Springer, vol. 21(3), pages 631-659, July.
    5. Steven Kou & Xianhua Peng, 2016. "On the Measurement of Economic Tail Risk," Operations Research, INFORMS, vol. 64(5), pages 1056-1072, October.
    6. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, December.
    7. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    8. Barbagli, Matteo & Vrins, Frédéric, 2025. "Efficient Monte Carlo estimation of credit concentration risk," LIDAM Discussion Papers LFIN 2025003, Université catholique de Louvain, Louvain Finance (LFIN).
    9. Laurent, Jean-Paul & Sestier, Michael & Thomas, Stéphane, 2016. "Trading book and credit risk: How fundamental is the Basel review?," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 211-223.
    10. Kao, Lie-Jane, 2015. "A portfolio-invariant capital allocation scheme penalizing concentration risk," Economic Modelling, Elsevier, vol. 51(C), pages 560-570.
    11. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524.
    12. Klaus Düllmann & Nancy Masschelein, 2006. "Sector Concentration in Loan Portfolios and Economic Capital," Working Paper Research 105, National Bank of Belgium.
    13. Avramidis, Panagiotis & Pasiouras, Fotios, 2015. "Calculating systemic risk capital: A factor model approach," Journal of Financial Stability, Elsevier, vol. 16(C), pages 138-150.
    14. Masschelein, Nancy & Düllmann, Klaus, 2006. "Sector concentration in loan portfolios and economic capital," Discussion Paper Series 2: Banking and Financial Studies 2006,09, Deutsche Bundesbank.
    15. Xin Huang & Hao Zhou & Haibin Zhu, 2012. "Systemic Risk Contributions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 55-83, October.
    16. Dhruv Bansal & Mayank Goud & Sourav Majumdar, 2026. "A stochastic correlation extension of the Vasicek credit risk model," Papers 2603.01109, arXiv.org, revised Mar 2026.
    17. Mendicino, Caterina & Nikolov, Kalin & Ramirez, Juan-Rubio & Suarez, Javier & Supera, Dominik, 2020. "Twin defaults and bank capital requirements," Working Paper Series 2414, European Central Bank.
    18. Rainer Masera, 2014. "CRR/CRD IV: the trees and the forest," PSL Quarterly Review, Economia civile, vol. 67(271), pages 381-422.
    19. Hengxin Cui & Ken Seng Tan & Fan Yang, 2024. "Portfolio credit risk with Archimedean copulas: asymptotic analysis and efficient simulation," Annals of Operations Research, Springer, vol. 332(1), pages 55-84, January.
    20. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1462-:d:1645779. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.