IDEAS home Printed from https://ideas.repec.org/a/vrs/ngooec/v64y2018i4p3-9n1.html
   My bibliography  Save this article

Modeling of Cash Flows from Nonperforming Loans in a Commercial Bank

Author

Listed:
  • Devjak Srečko

    (Rizikomanagement d.o.o., Velike Lašče, Slovenia)

Abstract

The purpose of this paper is to derive a model for calculation of maturities and volumes of repayments that a bank may expect from nonretail nonperforming loans (hereafter NPLs). Expected inflows from nonretail NPLs follow a probability distribution, defined by size and timing of historic repayments of NPLs. Empirical analysis has shown that probability distribution of expected inflows from nonretail NPLs considerably deviates from symmetric distribution and is asymmetric to the right. Accuracy of derived model depends upon available data in banks about NPLs by corporate sectors and recovery rates by time intervals. The model in this paper is in interest of any bank and in particular of banks with a higher fraction of NPLs in their loan portfolio. Contribution of this paper to the added value in the area of liquidity risk management in banks is high because the remaining literature does not deliver other models for the same purpose.

Suggested Citation

  • Devjak Srečko, 2018. "Modeling of Cash Flows from Nonperforming Loans in a Commercial Bank," Naše gospodarstvo/Our economy, Sciendo, vol. 64(4), pages 3-9, December.
    • Handle: RePEc:vrs:ngooec:v:64:y:2018:i:4:p:3-9:n:1
    DOI: 10.2478/ngoe-2018-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/ngoe-2018-0018
    Download Restriction: no
    File URL: https://libkey.io/10.2478/ngoe-2018-0018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Amogh Deshpande & Srikanth Iyer, 2009. "The credit risk + model with general sector correlations," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(2), pages 219-228, June.
    2. Black, Fischer & Cox, John C, 1976. "Valuing Corporate Securities: Some Effects of Bond Indenture Provisions," Journal of Finance, American Finance Association, vol. 31(2), pages 351-367, May.
    3. Bruche, Max & González-Aguado, Carlos, 2010. "Recovery rates, default probabilities, and the credit cycle," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 754-764, April.
    4. Levy, Haim & Sarnat, Marshall, 1977. "A Pedagogic Note on Alternative Formulations of the Goal of the Firm," The Journal of Business, University of Chicago Press, vol. 50(4), pages 526-528, October.
    5. Khieu, Hinh D. & Mullineaux, Donald J. & Yi, Ha-Chin, 2012. "The determinants of bank loan recovery rates," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 923-933.
    6. Xin Guo & Robert A. Jarrow & Yan Zeng, 2009. "Modeling The Recovery Rate In A Reduced Form Model," Mathematical Finance, Wiley Blackwell, vol. 19(1), pages 73-97, January.
    7. repec:bla:jfinan:v:53:y:1998:i:4:p:1363-1387 is not listed on IDEAS
    8. Mark S. Carey & Michael B. Gordy, 2007. "The bank as grim reaper: debt composition and recoveries on defaulted debt," Proceedings 1056, Federal Reserve Bank of Chicago.
    Full references (including those not matched with items on IDEAS)

    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. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    2. Mili, Medhi & Sahut, Jean-Michel & Teulon, Frédéric, 2018. "Modeling recovery rates of corporate defaulted bonds in developed and developing countries," Emerging Markets Review, Elsevier, vol. 36(C), pages 28-44.
    3. Zhang, Zhipeng, 2009. "Who Pulls the Plug? Theory and Evidence on Corporate Bankruptcy Decisions," MPRA Paper 17676, University Library of Munich, Germany, revised 05 Oct 2009.
    4. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    5. Dionne, Georges & Laajimi, Sadok, 2012. "On the determinants of the implied default barrier," Journal of Empirical Finance, Elsevier, vol. 19(3), pages 395-408.
    6. Meng-Jou Lu & Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle, 2017. "Copula-based factor model for credit risk analysis," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 949-971, November.
    7. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    8. Jean-David Fermanian, 2020. "On the Dependence between Default Risk and Recovery Rates in Structural Models," Annals of Economics and Statistics, GENES, issue 140, pages 45-82.
    9. Cosimo Munari & Stefan Weber & Lutz Wilhelmy, 2023. "Capital requirements and claims recovery: A new perspective on solvency regulation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 329-380, June.
    10. Sopitpongstorn, Nithi & Silvapulle, Param & Gao, Jiti & Fenech, Jean-Pierre, 2021. "Local logit regression for loan recovery rate," Journal of Banking & Finance, Elsevier, vol. 126(C).
    11. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    12. Satoshi Yamashita & Toshinao Yoshiba, 2010. "Analytical Solution for Expected Loss of a Collateralized Loan: A Square-root Intensity Process Negatively Correlated with Collateral Value," IMES Discussion Paper Series 10-E-10, Institute for Monetary and Economic Studies, Bank of Japan.
    13. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    14. Meng-Jou Lu & Cathy Yi-Hsuan Chen & Wolfgang Karl Hardle, 2020. "Copula-Based Factor Model for Credit Risk Analysis," Papers 2009.12092, arXiv.org, revised Oct 2020.
    15. Cangemi, Robert R. & Mason, Joseph R. & Pagano, Michael S., 2012. "Options-based structural model estimation of bond recovery rates," Journal of Financial Intermediation, Elsevier, vol. 21(3), pages 473-506.
    16. Michael Jacobs, Jr, 2011. "An option theoretic model for ultimate loss-given-default with systematic recovery risk and stochastic returns on defaulted debt," BIS Papers chapters, in: Bank for International Settlements (ed.), Portfolio and risk management for central banks and sovereign wealth funds, volume 58, pages 257-285, Bank for International Settlements.
    17. Ballotta, Laura & Fusai, Gianluca & Marazzina, Daniele, 2019. "Integrated structural approach to Credit Value Adjustment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1143-1157.
    18. Abu, Benjamin Musah & Domanban, Paul Bata & Haruna, Issahaku, 2017. "Microcredit Loan Repayment Default among Small Scale Enterprises: A Double Hurdle Approach," MPRA Paper 101576, University Library of Munich, Germany, revised 12 Mar 2017.
    19. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
    20. Abdelkader Derbali & Lamia Jamel, 2019. "Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Case of Greek Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(2), pages 711-733, June.

    More about this item

    Keywords

    bank; liquidity risk; cash flow modeling; credit risk; non-performing loans;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:vrs:ngooec:v:64:y:2018:i:4:p:3-9:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.