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A multi-factor approach for systematic default and recovery risk

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Abstract

The following article develops a simultaneous multi-factor model for defaults and recoveries. Applying this model, risk parameters can be forecast using systematic and idiosyncratic risk factors and their implied correlations. The theoretical framework is accompanied by an empirical analysis in which a negative correlation between defaults and recoveries over the business cycle is observed. In the study, default and recovery rates are modeled by business cycle indicators and the properties of the economic and regulatory capital given these risk drivers are shown.

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  • Daniel Roesch & Harald Scheule, 2005. "A multi-factor approach for systematic default and recovery risk," Published Paper Series 2005-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    • Handle: RePEc:uts:ppaper:2005-1
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    Cited by:

    1. Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
    2. Xiaolin Luo & Pavel V. Shevchenko, 2010. "Markov chain Monte Carlo estimation of default and recovery: dependent via the latent systematic factor," Papers 1011.2827, arXiv.org, revised Oct 2014.
    3. Pavel V. Shevchenko & Xiaolin Luo, 2011. "Dependent default and recovery: MCMC study of downturn LGD credit risk model," Papers 1112.5766, arXiv.org.
    4. Li, Hui, 2010. "Downturn LGD: A Spot Recovery Approach," MPRA Paper 71986, University Library of Munich, Germany, revised 30 Apr 2013.
    5. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    6. Rongda Chen & Ze Wang, 2013. "Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-9, July.
    7. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, January.
    8. Michael Jacobs, 2012. "An empirical study of the returns on defaulted debt," Applied Financial Economics, Taylor & Francis Journals, vol. 22(7), pages 563-579, April.
    9. Li, Hui, 2010. "Downturn LGD: A Spot Recovery Approach," MPRA Paper 20010, University Library of Munich, Germany.
    10. Rösch, Daniel & Scheule, Harald, 2012. "Capital incentives and adequacy for securitizations," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 733-748.
    11. Schläfer, Timo & Uhrig-Homburg, Marliese, 2014. "Is recovery risk priced?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 257-270.
    12. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    13. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    14. Daniel Rosch & Harald Scheule, 2008. "Credit Losses in Economic Downturns - Empirical Evidence for Hong Kong Mortgage Loans," Working Papers 152008, Hong Kong Institute for Monetary Research.
    15. Huong Dang, 2014. "A Competing Risks Dynamic Hazard Approach to Investigate the Insolvency Outcomes of Property-Casualty Insurers," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 39(1), pages 42-76, January.

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