IDEAS home Printed from https://ideas.repec.org/p/rtr/wpaper/0193.html
   My bibliography  Save this paper

Evaluation of Credit Risk Under Correlated Defaults: The Cross-Entropy Simulation Approach

Author

Listed:
  • Loretta Mastroeni
  • Giuseppe D'Acquisto
  • Maurizio Naldi

Abstract

Credit risk, associated to borrowers defaulting on their debts, is an ever growing source of concern for lenders. The presence of correlation among defaults may be described by the t-copula model. However, the typically large number of variables involved calls for a simulation approach. A simulation method, based on the use of the Cross-Entropy (CE) technique, is here proposed as an alternative to non-adaptive Importance Sampling (IS) techniques so far presented in the literature, the main advantage of CE being that it allows to deal easily with a wider range of probability models than ad hoc IS. The method is validated through a comparison of its results with the crude MonteCarlo and the Exponential Twist approaches. The proposed Cross-Entropy technique is shown to provide accurate results even when the sample size is several orders of magnitude smaller than the inverse of the probability to be estimated.

Suggested Citation

  • Loretta Mastroeni & Giuseppe D'Acquisto & Maurizio Naldi, 2014. "Evaluation of Credit Risk Under Correlated Defaults: The Cross-Entropy Simulation Approach," Departmental Working Papers of Economics - University 'Roma Tre' 0193, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0193
    as

    Download full text from publisher

    File URL: http://dipeco.uniroma3.it/db/docs/WP%20193.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. P. T. de Boer & D. P. Kroese & R. Y. Rubinstein, 2004. "A Fast Cross-Entropy Method for Estimating Buffer Overflows in Queueing Networks," Management Science, INFORMS, vol. 50(7), pages 883-895, July.
    2. Dietsch, Michel & Petey, Joel, 2004. "Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 773-788, April.
    3. Joshua Chan & Dirk Kroese, 2011. "Rare-event probability estimation with conditional Monte Carlo," Annals of Operations Research, Springer, vol. 189(1), pages 43-61, September.
    4. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    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. Francesco Dainelli & Francesco Giunta & Fabrizio Cipollini, 2013. "Determinants of SME credit worthiness under Basel rules: the value of credit history information," PSL Quarterly Review, Economia civile, vol. 66(264), pages 21-47.
    2. Lutz Hahnenstein, 2004. "Calibrating the CreditMetrics™ correlation concept — Empirical evidence from Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 18(4), pages 358-381, December.
    3. Gürtler, Marc & Heithecker, Dirk, 2005. "Systematic credit cycle risk of financial collaterals: Modelling and evidence," Working Papers FW15V2, Technische Universität Braunschweig, Institute of Finance.
    4. Lee, Shih-Cheng & Lin, Chien-Ting & Yang, Chih-Kai, 2011. "The asymmetric behavior and procyclical impact of asset correlations," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2559-2568, October.
    5. Annalisa Di Clemente, 2020. "Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(6), pages 1-23, June.
    6. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    7. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    8. Kern, Markus & Rudolph, Bernd, 2001. "Comparative analysis of alternative credit risk models: An application on German middle market loan portfolios," CFS Working Paper Series 2001/03, Center for Financial Studies (CFS).
    9. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    10. Paul S. Calem & Michael LaCour-Little, 2001. "Risk-based capital requirements for mortgage loans," Finance and Economics Discussion Series 2001-60, Board of Governors of the Federal Reserve System (U.S.).
    11. Christian Gouriéroux & Alain Monfort, 2017. "Composite Indirect Inference with Application," Working Papers 2017-07, Center for Research in Economics and Statistics.
    12. Koopman, Siem Jan & Lucas, Andre & Klaassen, Pieter, 2005. "Empirical credit cycles and capital buffer formation," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3159-3179, December.
    13. William Gornall & Ilya A. Strebulaev, 2013. "Financing as a Supply Chain: The Capital Structure of Banks and Borrowers," NBER Working Papers 19633, National Bureau of Economic Research, Inc.
    14. Greta Falavigna, 2006. "Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks," CERIS Working Paper 200610, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
    15. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    16. Correia, Maria & Kang, Johnny & Richardson, Scott, 2018. "Asset volatility," LSE Research Online Documents on Economics 84405, London School of Economics and Political Science, LSE Library.
    17. Kexue Liu & Jean Salvati & Renzo G Avesani & Alin T Mirestean, 2006. "Review and Implementation of Credit Risk Models of the Financial Sector Assessment Program (FSAP)," IMF Working Papers 2006/134, International Monetary Fund.
    18. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    19. Bag, Pinaki, 2010. "Exposure at Default Model for Contingent Credit Line," MPRA Paper 20387, University Library of Munich, Germany.
    20. Patrick Gagliardini, 2005. "Stochastic Migration Models with Application to Corporate Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(2), pages 188-226.

    More about this item

    Keywords

    Credit risk; Cross-Entropy; Copula models;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    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:rtr:wpaper:0193. 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: (Telephone for information). General contact details of provider: http://edirc.repec.org/data/dero3it.html .

    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 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.

    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.