IDEAS home Printed from
   My bibliography  Save this article

Default estimation for low-default portfolios


  • Kiefer, Nicholas M.


Risk managers at financial institutions are concerned with estimating default probabilities for asset groups both for internal risk control procedures and for regulatory compliance. Low-default assets pose an estimation problem that has attracted recent concern. The problem in default probability estimation for low-default portfolios is that there is little relevant historical data information. No amount of data data-processing can fix this problem. More information is required. Incorporating expert opinion formally is an attractive option. The probability (Bayesian) approach is proposed, its feasibility demonstrated, and its relation to supervisory requirements discussed.

Suggested Citation

  • Kiefer, Nicholas M., 2009. "Default estimation for low-default portfolios," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 164-173, January.
  • Handle: RePEc:eee:empfin:v:16:y:2009:i:1:p:164-173

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    1. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
    2. Giesecke, Kay & Weber, Stefan, 2004. "Cyclical correlations, credit contagion, and portfolio losses," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 3009-3036, December.
    3. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    2. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    3. Yi-Ping Chang & Chih-Tun Yu, 2014. "Bayesian confidence intervals for probability of default and asset correlation of portfolio credit risk," Computational Statistics, Springer, vol. 29(1), pages 331-361, February.
    4. Walter Orth, 2013. "Default probability estimation in small samples--with an application to sovereign bonds," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1891-1902, December.
    5. Kiefer, Nicholas M., 2009. "Incentive-Compatible Elicitation of Quantiles," Working Papers 09-13, Cornell University, Center for Analytic Economics.
    6. Orth, Walter, 2011. "Default probability estimation in small samples - with an application to sovereign bonds," MPRA Paper 33778, University Library of Munich, Germany.
    7. Blümke, Oliver, 2018. "On the cyclicality of default rates of banks: A comparative study of the asset correlation and diversification effects," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 65-77.
    8. do Nascimento, José Cláudio, 2021. "The personal wealth importance to the intertemporal choice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    9. Feixue Huang & Yan He, 2010. "Enactment of Default Point in KMV Model on CMBC, SPDB, CMB, Huaxia Bank and SDB," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 1(1), pages 30-36, December.
    10. Dirk Tasche, 2011. "Bayesian estimation of probabilities of default for low default portfolios," Papers 1112.5550,, revised Aug 2013.
    11. Gourieroux, C. & Monfort, A., 2021. "Model risk management: Valuation and governance of pseudo-models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 1-22.
    12. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    13. Oliver Blümke, 2020. "Estimating the probability of default for no‐default and low‐default portfolios," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(1), pages 89-107, January.
    14. Gourieroux, Christian & Tiomo, Andre, 2019. "The Evaluation of Model Risk for Probability of Default and Expected Loss," MPRA Paper 95795, University Library of Munich, Germany.
    15. Wei, Li & Yuan, Zhongyi, 2016. "The loss given default of a low-default portfolio with weak contagion," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 113-123.
    16. Yajiao Tang & Junkai Ji & Yulin Zhu & Shangce Gao & Zheng Tang & Yuki Todo, 2019. "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-21, August.
    17. Orth, Walter, 2011. "Default probability estimation in small samples: With an application to sovereign bonds," Discussion Papers in Econometrics and Statistics 5/11, University of Cologne, Institute of Econometrics and Statistics.
    18. Krüger, Steffen & Oehme, Toni & Rösch, Daniel & Scheule, Harald, 2018. "A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 246-262.

    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. Helmut Elsinger & Alfred Lehar & Martin Summer, 2006. "Risk Assessment for Banking Systems," Management Science, INFORMS, vol. 52(9), pages 1301-1314, September.
    2. Cumhur ÞAHÝN & Hüseyin ALTAY, 2016. "Examination of the Relationship between Turkey’s Credit Default Swap (CDS) Points and Unemployment," Eurasian Business & Economics Journal, Eurasian Academy Of Sciences, vol. 4(4), pages 52-67, January.
    3. Jin-Chuan Duan & Weimin Miao, 2016. "Default Correlations and Large-Portfolio Credit Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 536-546, October.
    4. Tingqiang Chen & Binqing Xiao & Haifei Liu, 2018. "Credit Risk Contagion in an Evolving Network Model Integrating Spillover Effects and Behavioral Interventions," Complexity, Hindawi, vol. 2018, pages 1-16, March.
    5. Egloff, Daniel & Leippold, Markus & Vanini, Paolo, 2007. "A simple model of credit contagion," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2475-2492, August.
    6. Qian Qian & Yang Yang & Zong-Fang Zhou, 2019. "Research on Trade Credit Spreading and Credit Risk within the Supply Chain," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 389-411, January.
    7. Giesecke, Kay & Weber, Stefan, 2006. "Credit contagion and aggregate losses," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 741-767, May.
    8. Serge Darolles & Patrick Gagliardini & Christian Gouriéroux, 2012. "Survival of Hedge Funds : Frailty vs Contagion," Working Papers 2012-36, Center for Research in Economics and Statistics.
    9. Xiaowei Ding & Kay Giesecke & Pascal I. Tomecek, 2009. "Time-Changed Birth Processes and Multiname Credit Derivatives," Operations Research, INFORMS, vol. 57(4), pages 990-1005, August.
    10. Areski Cousin & Diana Dorobantu & Didier Rullière, 2013. "An extension of Davis and Lo's contagion model," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 407-420, February.
    11. Diana Barro & Antonella Basso, 2008. "A network of business relations to model counterparty risk," Working Papers 171, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    12. Zedginidze Zviad, 2012. "Linking Macroeconomic Dynamics to Georgian Credit Portfolio Risk," EERC Working Paper Series 12/07e, EERC Research Network, Russia and CIS.
    13. Andre R. Neveu, 2018. "A survey of network-based analysis and systemic risk measurement," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 241-281, July.
    14. Xin Huang, 2020. "The risk of betting on risk: Conditional variance and correlation of bank credit default swaps," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 710-721, May.
    15. Anand, Kartik & Gai, Prasanna & Kapadia, Sujit & Brennan, Simon & Willison, Matthew, 2013. "A network model of financial system resilience," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 219-235.
    16. Didier Cossin & Henry Schellhorn, 2007. "Credit Risk in a Network Economy," Management Science, INFORMS, vol. 53(10), pages 1604-1617, October.
    17. Gagliardini, Patrick & Gouriéroux, Christian, 2013. "Correlated risks vs contagion in stochastic transition models," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2241-2269.
    18. David E Allen & Robert Powell, 2012. "The fluctuating default risk of Australian banks," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 297-325, August.
    19. Bäuerle Nicole & Schmock Uwe, 2012. "Dependence properties of dynamic credit risk models," Statistics & Risk Modeling, De Gruyter, vol. 29(3), pages 243-268, August.
    20. Justin Sirignano & Kay Giesecke, 2019. "Risk Analysis for Large Pools of Loans," Management Science, INFORMS, vol. 65(1), pages 107-121, January.

    More about this item


    Bayesian inference Bayesian estimation Expert information Basel II Risk management;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • G18 - Financial Economics - - General Financial Markets - - - 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


    Access and download statistics


    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:eee:empfin:v:16:y:2009:i:1:p:164-173. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: Catherine Liu (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.