IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v9y2016i2p4-d71610.html
   My bibliography  Save this article

Application of Vine Copulas to Credit Portfolio Risk Modeling

Author

Listed:
  • Marco Geidosch

    (UniCredit Bank AG, Munich, Germany)

  • Matthias Fischer

    (Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Germany)

Abstract

In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50 and the S&P 500 companies, respectively. Our study includes D-vines and R-vines where the bivariate building blocks are chosen from the Gaussian, the t and the Clayton family. Our findings are (i) the conventional Gauss copula is deficient in modeling the dependence structure of a credit portfolio and economic capital is seriously underestimated; (ii) D-vine structures offer a better statistical fit to the data than classical copulas, but underestimate economic capital compared to R-vines; (iii) when mixing different copula families in an R-vine structure, the best statistical fit to the data can be achieved which corresponds to the most reliable estimate for economic capital.

Suggested Citation

  • Marco Geidosch & Matthias Fischer, 2016. "Application of Vine Copulas to Credit Portfolio Risk Modeling," JRFM, MDPI, vol. 9(2), pages 1-15, June.
    • Handle: RePEc:gam:jjrfmx:v:9:y:2016:i:2:p:4-:d:71610
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/9/2/4/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/9/2/4/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthias Fischer & Christian Kock & Stephan Schluter & Florian Weigert, 2009. "An empirical analysis of multivariate copula models," Quantitative Finance, Taylor & Francis Journals, vol. 9(7), pages 839-854.
    2. Robert A. Jarrow & Stuart M. Turnbull, 2008. "Pricing Derivatives on Financial Securities Subject to Credit Risk," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 17, pages 377-409, World Scientific Publishing Co. Pte. Ltd..
    3. 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.
    4. Young Ho Eom, 2004. "Structural Models of Corporate Bond Pricing: An Empirical Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 499-544.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. John C. Hull, 2009. "The Credit Crunch of 2007: What Went Wrong? Why? What Lessons Can be Learned?," World Scientific Book Chapters, in: Douglas D Evanoff & Philipp Hartmann & George G Kaufman (ed.), The First Credit Market Turmoil Of The 21st Century Implications for Public Policy, chapter 11, pages 161-174, World Scientific Publishing Co. Pte. Ltd..
    7. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    8. Aleksey Min & Claudia Czado, 2010. "Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions," Journal of Financial Econometrics, Oxford University Press, vol. 8(4), pages 511-546, Fall.
    9. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    10. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    11. Jing-Zhi Huang & Ming Huang, 2012. "How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 2(2), pages 153-202.
    12. Dorota Kurowicka, 2010. "Optimal Truncation of Vines," World Scientific Book Chapters, in: Dorota Kurowicka & Harry Joe (ed.), Dependence Modeling Vine Copula Handbook, chapter 11, pages 233-247, World Scientific Publishing Co. Pte. Ltd..
    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. David E. Allen & Michael McAleer & Abhay K. Singh, 2017. "Risk Measurement and Risk Modelling Using Applications of Vine Copulas," Sustainability, MDPI, vol. 9(10), pages 1-34, September.
    2. Fernanda Maria Müller & Marcelo Brutti Righi, 2024. "Comparison of Value at Risk (VaR) Multivariate Forecast Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 75-110, January.
    3. Matthias Fischer & Thorsten Moser & Marius Pfeuffer, 2018. "A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations," Risks, MDPI, vol. 6(4), pages 1-28, December.
    4. Vahidin Jeleskovic & Mirko Meloni & Zahid Irshad Younas, 2020. "Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations," MAGKS Papers on Economics 202034, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    5. Cyprian Omari & Peter Mwita & Anthony Waititu, 2019. "Conditional Dependence Modelling with Regular Vine Copulas," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(1), pages 1-5.
    6. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    7. Fernanda Maria Müller & Thalles Weber Gössling & Samuel Solgon Santos & Marcelo Brutti Righi, 2024. "A comparison of Range Value at Risk (RVaR) forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 509-543, April.
    8. Huawei Li & Guohe Huang & Yongping Li & Jie Sun & Pangpang Gao, 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    9. Hüttner, Amelie & Scherer, Matthias & Gräler, Benedikt, 2020. "Geostatistical modeling of dependent credit spreads: Estimation of large covariance matrices and imputation of missing data," Journal of Banking & Finance, Elsevier, vol. 118(C).
    10. Lei, Lei & Peng, Yijie & Fu, Michael C. & Hu, Jian-Qiang, 2023. "Copula sensitivity analysis for portfolio credit derivatives," European Journal of Operational Research, Elsevier, vol. 308(1), pages 455-466.
    11. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    12. Bukre Yildirim Kulekci & Gulden Poyraz & Ismail Gur & Ozan Evkaya, 2023. "Dependence Analysis of the ISE100 Banking Sector Using Vine Copula," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(73-1), pages 55-81, June.
    13. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.

    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. Pasquale De Luca, 2017. "The Company Fundamental Analysis and the Default Risk Ratio," International Journal of Business and Management, Canadian Center of Science and Education, vol. 12(10), pages 1-79, September.
    2. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5(1), pages 49-72.
    3. Augustin, Patrick & Subrahmanyam, Marti G. & Tang, Dragon Yongjun & Wang, Sarah Qian, 2014. "Credit Default Swaps: A Survey," Foundations and Trends(R) in Finance, now publishers, vol. 9(1-2), pages 1-196, December.
    4. 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.
    5. 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.
    6. Brian BARNARD, 2018. "Rating Migration and Bond Valuation: Ahistorical Interest Rate and Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 6(1), pages 16-30.
    7. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5, pages 49-72.
    8. Perrakis, Stylianos & Zhong, Rui, 2015. "Credit spreads and state-dependent volatility: Theory and empirical evidence," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 215-231.
    9. 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.
    10. Jang, Bong-Gyu & Rhee, Yuna & Yoon, Ji Hee, 2016. "Business cycle and credit risk modeling with jump risks," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 15-36.
    11. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    12. Chen, An-Sing & Chu, Hsiang-Hui & Hung, Pi-Hsia & Cheng, Miao-Sih, 2020. "Financial risk and acquirers' stockholder wealth in mergers and acquisitions," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    13. 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.
    14. 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.
    15. Di Bu & Simone Kelly & Yin Liao & Qing Zhou, 2018. "A hybrid information approach to predict corporate credit risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1062-1078, September.
    16. Maclachlan, Iain C, 2007. "An empirical study of corporate bond pricing with unobserved capital structure dynamics," MPRA Paper 28416, University Library of Munich, Germany.
    17. Maria Carmen Badia Batlle & M. Mercedes Galisteo Rodriguez & M. Teresa Preixens Benedicto, 2006. "Un modelo de riesgo de credito basado en opciones compuestas con barrera. Aplicacion al mercado continuo espanol," Working Papers in Economics 156, Universitat de Barcelona. Espai de Recerca en Economia.
    18. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    19. Bank for International Settlements, 2011. "Portfolio and risk management for central banks and sovereign wealth funds," BIS Papers, Bank for International Settlements, number 58.
    20. 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.

    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:gam:jjrfmx:v:9:y:2016:i:2:p:4-:d:71610. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.