IDEAS home Printed from https://ideas.repec.org/p/hkm/wpaper/152008.html
   My bibliography  Save this paper

Credit Losses in Economic Downturns - Empirical Evidence for Hong Kong Mortgage Loans

Author

Listed:
  • Daniel Rosch

    (Leibniz University of Hannover)

  • Harald Scheule

    (University of Melbourne)

Abstract

Recent studies find a positive correlation between default and loss given default rates of credit portfolios. In response, financial regulators require financial institutions to base their capital on the 'Downturn' loss rate given default which is also known as Downturn LGD. This article proposes a concept for the Downturn LGD which incorporates econometric properties of credit risk as well as the information content of default and loss given default models. The concept is compared to an alternative proposal by the Department of the Treasury, the Federal Reserve System and the Federal Insurance Corporation. An empirical analysis is provided for Hong Kong mortgage loan portfolios.

Suggested Citation

  • Daniel Rosch & Harald Scheule, 2008. "Credit Losses in Economic Downturns - Empirical Evidence for Hong Kong Mortgage Loans," Working Papers 152008, Hong Kong Institute for Monetary Research.
  • Handle: RePEc:hkm:wpaper:152008
    as

    Download full text from publisher

    File URL: http://www.hkimr.org/uploads/publication/158/ub_full_0_2_184_hkimr-no-15_bw.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Laeven, Luc & Majnoni, Giovanni, 2003. "Loan loss provisioning and economic slowdowns: too much, too late?," Journal of Financial Intermediation, Elsevier, vol. 12(2), pages 178-197, April.
    2. Daniel Rösch & Harald Scheule, 2006. "A Multi-Factor Approach for Systematic Default and Recovery Risk," Springer Books, in: Bernd Engelmann & Robert Rauhmeier (ed.), The Basel II Risk Parameters, chapter 0, pages 105-125, Springer.
    3. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March.
    4. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    5. 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.
    6. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    7. Alfred Hamerle & Thilo Liebig & Harald Scheule, 2006. "Forecasting credit event frequency – empirical evidence for West German firms," Published Paper Series 2006-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    8. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390, arXiv.org, revised Feb 2004.
    9. repec:uts:ppaper:v:15:y:2005:i:3:p:63-75 is not listed on IDEAS
    10. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    11. Mark Carey, 1998. "Credit Risk in Private Debt Portfolios," Journal of Finance, American Finance Association, vol. 53(4), pages 1363-1387, August.
    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. Rösch, Daniel & Scheule, Harald, 2009. "The Empirical Relation between Credit Quality, Recovery and Correlation," Hannover Economic Papers (HEP) dp-418, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    3. Daniel Rösch & Harald Scheule, 2011. "Securitization rating performance and agency incentives," BIS Papers chapters, in: Bank for International Settlements (ed.), Portfolio and risk management for central banks and sovereign wealth funds, volume 58, pages 287-314, Bank for International Settlements.
    4. Daniel R÷Sch & Harald Scheule, 2010. "Downturn Credit Portfolio Risk, Regulatory Capital and Prudential Incentives-super-," International Review of Finance, International Review of Finance Ltd., vol. 10(Financial), pages 185-207.
    5. Bank for International Settlements, 2011. "Portfolio and risk management for central banks and sovereign wealth funds," BIS Papers, Bank for International Settlements, number 58.
    6. Benjamin Bade & Daniel Rösch & Harald Scheule, 2011. "Default and Recovery Risk Dependencies in a Simple Credit Risk Model," European Financial Management, European Financial Management Association, vol. 17(1), pages 120-144, January.
    7. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, June.
    8. Rösch, Daniel & Scheule, Harald, 2009. "Credit rating impact on CDO evaluation," Global Finance Journal, Elsevier, vol. 19(3), pages 235-251.
    9. Gürtler, Marc & Heithecker, Dirk, 2005. "Systematic credit cycle risk of financial collaterals: Modelling and evidence," Working Papers FW15V2, Technische Universität Braunschweig, Institute of Finance.
    10. Annalisa Di Clemente, 2013. "Considering the dependence between the credit loss severity and the probability of default in the estimate of portfolio credit risk: an experimental analysis," STUDI ECONOMICI, FrancoAngeli Editore, vol. 2013(109), pages 5-24.
    11. Rösch, Daniel & Scheule, Harald, 2012. "Capital incentives and adequacy for securitizations," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 733-748.
    12. Magdalena Pisa & Dennis Bams & Christian Wolff, 2012. "Modeling default correlation in a US retail loan portfolio," LSF Research Working Paper Series 12-19, Luxembourg School of Finance, University of Luxembourg.
    13. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
    14. Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series 168, Sveriges Riksbank (Central Bank of Sweden).
    15. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    16. Gürtler, Marc & Heithecker, Dirk, 2004. "Modellkonsistente Bestimmung des LGD im IRB-Ansatz von Basel II," Working Papers FW08V3, Technische Universität Braunschweig, Institute of Finance.
    17. Dietsch, Michel & Düllmann, Klaus & Fraisse, Henri & Koziol, Philipp & Ott, Christine, 2016. "Support for the SME supporting factor: Multi-country empirical evidence on systematic risk factor for SME loans," Discussion Papers 45/2016, Deutsche Bundesbank.
    18. Varotto, Simone, 2012. "Stress testing credit risk: The Great Depression scenario," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3133-3149.
    19. Steffi Höse & Stefan Huschens, 2011. "Confidence Intervals for Asset Correlations in the Asymptotic Single Risk Factor Model," Operations Research Proceedings, in: Bo Hu & Karl Morasch & Stefan Pickl & Markus Siegle (ed.), Operations Research Proceedings 2010, pages 111-116, Springer.
    20. García-Céspedes, Rubén & Moreno, Manuel, 2014. "Estimating the distribution of total default losses on the Spanish financial system," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 242-261.

    More about this item

    Keywords

    Basel II; Business Cycle; Capital Adequacy; Correlation; Credit Risk; Economic Downturn; Expected Loss; Fixed Income; Loss Given Default; Probability of Default; Value-at-Risk;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hkm:wpaper:152008. 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: HKIMR (email available below). General contact details of provider: https://edirc.repec.org/data/hkimrhk.html .

    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.