IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0202.html
   My bibliography  Save this article

A copula-based approach to portfolio credit risk modeling

Author

Listed:
  • Bologov , Yaroslav

    (Moscow State University)

Abstract

Considering correlations between entries of credit portfolio is an important objective when estimating credit risk. This paper aims to construct a multivariate model of credit losses examining a portfolio composed of loans to a set of kinds of business. The paper also introduces the method of credit risk calculation via copulas, gamma distribution and kernel estimates. Empirical application of the introduced method is realized by using a historical loss data provided by one of the Moscow credit banks.

Suggested Citation

  • Bologov , Yaroslav, 2013. "A copula-based approach to portfolio credit risk modeling," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 29(1), pages 45-66.
  • Handle: RePEc:ris:apltrx:0202
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2013_1_45-66.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Kojadinovic, Ivan & Yan, Jun, 2010. "Modeling Multivariate Distributions with Continuous Margins Using the copula R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i09).
    3. Kritski, Oleg & Ulyanova, Marina, 2007. "Assessment of Multivariate Financial Risks of a Stock Share Portfolio," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 8(4), pages 3-17.
    4. Andre Lucas & Pieter Klaassen & Peter Spreij & Stefan Straetmans, 2003. "Tail behaviour of credit loss distributions for general latent factor models," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(4), pages 337-357.
    5. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    6. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
    7. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    8. Blagoveschensky, Yury, 2012. "Basics of copula’s theory," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 113-130.
    9. Fantazzini , Dean, 2009. "Credit Risk Management (Cont.)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 105-138.
    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. Дробыш И.И., 2016. "Сравнительный анализ методов оценки рыночного риска, основанных на величине Value at Risk," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 52(4), pages 74-93, октябрь.

    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. 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.
    2. Казакова К.А. & Князев А.Г. & Лепёхин О.А., 2015. "Оптимальный размер банковского резерва: прогноз просроченной кредитной задолженности с использованием копулярных моделей. Optimum volume of bank reserve: forecasting of overdue credit indebtedness usi," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 15(4), pages 59-76.
    3. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    4. Annaert, Jan & De Ceuster, Marc & Van Roy, Patrick & Vespro, Cristina, 2013. "What determines Euro area bank CDS spreads?," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 444-461.
    5. Maclachlan, Iain C, 2007. "An empirical study of corporate bond pricing with unobserved capital structure dynamics," MPRA Paper 28416, University Library of Munich, Germany.
    6. Liuren Wu & Frank X. Zhang, 2005. "A no-arbitrage analysis of economic determinants of the credit spread term structure," Finance and Economics Discussion Series 2005-59, Board of Governors of the Federal Reserve System (U.S.).
    7. Mencía, Javier, 2012. "Assessing the risk-return trade-off in loan portfolios," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1665-1677.
    8. Liuren Wu & Frank Xiaoling Zhang, 2008. "A No-Arbitrage Analysis of Macroeconomic Determinants of the Credit Spread Term Structure," Management Science, INFORMS, vol. 54(6), pages 1160-1175, June.
    9. 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.
    10. Schäfer, Rudi & Koivusalo, Alexander F.R., 2013. "Dependence of defaults and recoveries in structural credit risk models," Economic Modelling, Elsevier, vol. 30(C), pages 1-9.
    11. Stephanie Heck, 2022. "Corporate bond yields and returns: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(2), pages 179-201, June.
    12. Jansen, Jeroen & Das, Sanjiv R. & Fabozzi, Frank J., 2018. "Local volatility and the recovery rate of credit default swaps," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 1-29.
    13. Antje Berndt & Rohan Douglas & Darrell Duffie & Mark Ferguson, "undated". "Measuring Default Risk Premia from Default Swap Rates and EDFs," GSIA Working Papers 2006-E31, Carnegie Mellon University, Tepper School of Business.
    14. Stephen Zamore & Kwame Ohene Djan & Ilan Alon & Bersant Hobdari, 2018. "Credit Risk Research: Review and Agenda," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(4), pages 811-835, March.
    15. Seidler, Jakub & Horvath, Roman & Jakubík, Petr, 2009. "Estimating expected loss given default in an emerging market: the case of Czech Republic," Journal of Financial Transformation, Capco Institute, vol. 27, pages 103-107.
    16. Брагин Антон Игоревич & Кузнецов Евгений Николаевич, 2011. "Анализ Значений Суверенного Кредитного Рейтинга И Его Моделирование," Российский внешнеэкономический вестник, CyberLeninka;Государственное образовательное учреждение Высшего профессионального образования Всероссийская академия внешней торговли Минэкономразвития России, vol. 2011(12), pages 21-36.
    17. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, September.
    18. Zhang, Zhipeng, 2009. "Recovery Rates and Macroeconomic Conditions: The Role of Loan Covenants," MPRA Paper 17521, University Library of Munich, Germany.
    19. Osadchiy, Maksim & Sidorov, Alexander, 2020. "Hacked AIRB Black Box," MPRA Paper 100801, University Library of Munich, Germany.
    20. Duffie, Darrell, 2005. "Credit risk modeling with affine processes," Journal of Banking & Finance, Elsevier, vol. 29(11), pages 2751-2802, November.

    More about this item

    Keywords

    credit risk; credit bank; multivariate modeling; copula; extreme value theory; kernel smoothing;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    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:ris:apltrx:0202. 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: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    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.