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Credit spread approximation and improvement using random forest regression

Author

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  • Mathieu Mercadier

    (LAPE, Université de Limoges)

  • 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.

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  • Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit spread approximation and improvement using random forest regression," Post-Print hal-03241566, HAL.
    • Handle: RePEc:hal:journl:hal-03241566
    DOI: 10.1016/j.ejor.2019.02.005
    Note: View the original document on HAL open archive server: https://uca.hal.science/hal-03241566
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    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. Brummelhuis, Raymond & Luo, Zhongmin, 2017. "CDS Rate Construction Methods by Machine Learning Techniques," MPRA Paper 79194, University Library of Munich, Germany.
    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. Tomohiro Ando, 2014. "Bayesian corporate bond pricing and credit default swap premium models for deriving default probabilities and recovery rates," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 454-465, March.
    6. Chunsheng Zhou, 1997. "A jump-diffusion approach to modeling credit risk and valuing defaultable securities," Finance and Economics Discussion Series 1997-15, Board of Governors of the Federal Reserve System (U.S.).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    12. 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.
    13. 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.
    14. Zhou, Chunsheng, 2001. "An Analysis of Default Correlations and Multiple Defaults," The Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 555-576.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Zhou, Chunsheng, 2001. "The term structure of credit spreads with jump risk," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 2015-2040, November.
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    Keywords

    Risk Analysis; Credit Default Swaps; Random Forests; Finance; Structural Model;
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