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Dependence of defaults and recoveries in structural credit risk models

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  • Schäfer, Rudi
  • Koivusalo, Alexander F.R.

Abstract

The current research on credit risk is primarily focused on modelling default probabilities. Recovery rates are often treated as an afterthought; they are modelled independently, in many cases they are even assumed to be constant. This despite their pronounced effect on the tail of the loss distribution. Here, we take a step back, historically, and start again from the Merton model, where defaults and recoveries are both determined by an underlying process. Hence, they are intrinsically connected. For the diffusion process, we can derive the functional relation between expected recovery rate and default probability. This relation depends on a single parameter only. In Monte Carlo simulations we find that the same functional dependence also holds for jump-diffusion and GARCH processes. We discuss how to incorporate this structural recovery rate into reduced-form models, in order to restore essential structural information which is usually neglected in the reduced-form approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecmode:v:30:y:2013:i:c:p:1-9
    DOI: 10.1016/j.econmod.2012.08.033
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    References listed on IDEAS

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    Cited by:

    1. Frontczak, Robert & Rostek, Stefan, 2015. "Modeling loss given default with stochastic collateral," Economic Modelling, Elsevier, vol. 44(C), pages 162-170.
    2. Alexander F. R. Koivusalo & Rudi Schafer, 2011. "Calibration of structural and reduced-form recovery models," Papers 1102.4864, arXiv.org.
    3. Abdelkader Derbali & Lamia Jamel, 2019. "Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Case of Greek Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(2), pages 711-733, June.
    4. 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.
    5. Michael C Münnix & Rudi Schäfer & Thomas Guhr, 2014. "A Random Matrix Approach to Credit Risk," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-9, May.

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    More about this item

    Keywords

    Credit risk; Loss distribution; Value at risk; Expected tail loss; Stochastic processes;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • H81 - Public Economics - - Miscellaneous Issues - - - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts

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