IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2106.07358.html
   My bibliography  Save this paper

Credit spread approximation and improvement using random forest regression

Author

Listed:
  • Mathieu Mercadier
  • Jean-Pierre Lardy

Abstract

Credit Default Swap (CDS) levels provide a market appreciation of companies' default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and proprietary approximations currently in use. This Equity-to-Credit formula (E2C), inspired by CreditGrades, obtains better CDS approximations, according to empirical analyses based on a large sample spanning 2016-2018. A random forest regression run with this E2C formula and selected additional financial data results in an 87.3% out-of-sample accuracy in CDS approximations. The transparency property of this algorithm confirms the predominance of the E2C estimate, and the impact of companies' debt rating and size, in predicting their CDS.

Suggested Citation

  • Mathieu Mercadier & Jean-Pierre Lardy, 2021. "Credit spread approximation and improvement using random forest regression," Papers 2106.07358, arXiv.org.
  • Handle: RePEc:arx:papers:2106.07358
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2106.07358
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rama Cont & Andreea Minca, 2016. "Credit default swaps and systemic risk," Annals of Operations Research, Springer, vol. 247(2), pages 523-547, December.
    2. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    3. Raymond Brummelhuis & Zhongmin Luo, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," Papers 1705.06899, arXiv.org.
    4. Black, Fischer & Cox, John C, 1976. "Valuing Corporate Securities: Some Effects of Bond Indenture Provisions," Journal of Finance, American Finance Association, vol. 31(2), pages 351-367, May.
    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. Zhou, Chunsheng, 2001. "An Analysis of Default Correlations and Multiple Defaults," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 555-576.
    7. Joao Teixeira, 2007. "An empirical analysis of structural models of corporate debt pricing," Applied Financial Economics, Taylor & Francis Journals, vol. 17(14), pages 1141-1165.
    8. George Chalamandaris & Nikos E. Vlachogiannakis, 2018. "Are financial ratios relevant for trading credit risk? Evidence from the CDS market," Annals of Operations Research, Springer, vol. 266(1), pages 395-440, July.
    9. Irresberger, Felix & Weiß, Gregor N.F. & Gabrysch, Janet & Gabrysch, Sandra, 2018. "Liquidity tail risk and credit default swap spreads," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1137-1153.
    10. Dimitrios Koutmos, 2018. "Interdependencies between CDS spreads in the European Union: Is Greece the black sheep or black swan?," Annals of Operations Research, Springer, vol. 266(1), pages 441-498, July.
    11. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    12. Andreas Behr & Jurij Weinblat, 2017. "Default Patterns in Seven EU Countries: A Random Forest Approach," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 24(2), pages 181-222, May.
    13. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    14. Guarin, Alexander & Liu, Xiaoquan & Ng, Wing Lon, 2011. "Enhancing credit default swap valuation with meshfree methods," European Journal of Operational Research, Elsevier, vol. 214(3), pages 805-813, November.
    15. Zhou, Chunsheng, 2001. "The term structure of credit spreads with jump risk," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 2015-2040, November.
    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. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
    2. Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit spread approximation and improvement using random forest regression," Post-Print hal-03241566, HAL.
    3. Nystrom, Kaj & Skoglund, Jimmy, 2006. "A credit risk model for large dimensional portfolios with application to economic capital," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2163-2197, August.
    4. Ming Xi Huang, 2010. "Modelling Default Correlations in a Two-Firm Model with Dynamic Leverage Ratios," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2010.
    5. Amelie Hüttner & Matthias Scherer, 2016. "A note on the valuation of CDS options and extension risk in a structural model with jumps," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 1-16, June.
    6. Han-Hsing Lee & Kuanyu Shih & Kehluh Wang, 2016. "Measuring sovereign credit risk using a structural model approach," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1097-1128, November.
    7. Alexander, Carol & Kaeck, Andreas, 2008. "Regime dependent determinants of credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1008-1021, June.
    8. Forte, Santiago & Lovreta, Lidija, 2012. "Endogenizing exogenous default barrier models: The MM algorithm," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1639-1652.
    9. Zhou, Chunsheng, 2001. "The term structure of credit spreads with jump risk," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 2015-2040, November.
    10. Hamerle, Alfred & Liebig, Thilo & Rösch, Daniel, 2003. "Credit Risk Factor Modeling and the Basel II IRB Approach," Discussion Paper Series 2: Banking and Financial Studies 2003,02, Deutsche Bundesbank.
    11. Luca Vincenzo Ballestra & Graziella Pacelli, 2009. "A Numerical Method to Price Defaultable Bonds Based on the Madan and Unal Credit Risk Model," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(1), pages 17-36.
    12. Batten, Jonathan & Hogan, Warren, 2002. "A perspective on credit derivatives," International Review of Financial Analysis, Elsevier, vol. 11(3), pages 251-278.
    13. 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.
    14. Sheen Liu & Howard Qi & Jian Shi & Yan Alice Xie, 2015. "Inferring Default Correlation from Equity Return Correlation," European Financial Management, European Financial Management Association, vol. 21(2), pages 333-359, March.
    15. Hackbarth, Dirk & Miao, Jianjun & Morellec, Erwan, 2006. "Capital structure, credit risk, and macroeconomic conditions," Journal of Financial Economics, Elsevier, vol. 82(3), pages 519-550, December.
    16. Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank.
    17. Jean-David Fermanian, 2020. "On the Dependence between Default Risk and Recovery Rates in Structural Models," Annals of Economics and Statistics, GENES, issue 140, pages 45-82.
    18. Chang, Sean Tat & Ross, Donald, 2016. "Debt covenants and credit spread valuation: The special case of Chinese global bonds," Global Finance Journal, Elsevier, vol. 30(C), pages 27-44.
    19. Hett, Florian & Schmidt, Alexander, 2017. "Bank rescues and bailout expectations: The erosion of market discipline during the financial crisis," Journal of Financial Economics, Elsevier, vol. 126(3), pages 635-651.
    20. Mark B. Wise & Vineer Bhansali, 2002. "Implications of Correlated Default For Portfolio Allocation To Corporate Bonds," Papers nlin/0209010, arXiv.org.

    More about this item

    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:arx:papers:2106.07358. 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: http://arxiv.org/ .

    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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.