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Credit risk—A structural model with jumps and correlations

Author

Listed:
  • Schäfer, Rudi
  • Sjölin, Markus
  • Sundin, Andreas
  • Wolanski, Michal
  • Guhr, Thomas

Abstract

We set up a structural model to study credit risk for a portfolio containing several or many credit contracts. The model is based on a jump-diffusion process for the risk factors, i.e. for the company assets. We also include correlations between the companies. We discuss that models of this type have much in common with other problems in statistical physics and in the theory of complex systems. We study a simplified version of our model analytically. Furthermore, we perform extensive numerical simulations for the full model. The observables are the loss distribution of the credit portfolio, its moments and other quantities derived thereof. We compile detailed information about the parameter dependence of these observables. In the course of setting up and analyzing our model, we also give a review of credit risk modeling for a physics audience.

Suggested Citation

  • Schäfer, Rudi & Sjölin, Markus & Sundin, Andreas & Wolanski, Michal & Guhr, Thomas, 2007. "Credit risk—A structural model with jumps and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 533-569.
    • Handle: RePEc:eee:phsmap:v:383:y:2007:i:2:p:533-569
    DOI: 10.1016/j.physa.2007.04.053
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    Citations

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    Cited by:

    1. 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.
    2. Lee, Sangwook & Kim, Min Jae & Lee, Sun Young & Kim, Soo Yong & Ban, Joon Hwa, 2013. "The effect of the subprime crisis on the credit risk in global scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2060-2071.
    3. Xiao, Weilin & Zhang, Xili, 2016. "Pricing equity warrants with a promised lowest price in Merton’s jump–diffusion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 219-238.
    4. Barro, Diana & Basso, Antonella, 2010. "Credit contagion in a network of firms with spatial interaction," European Journal of Operational Research, Elsevier, vol. 205(2), pages 459-468, September.
    5. Sandoval, Leonidas, 2012. "Pruning a minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2678-2711.
    6. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
    7. Münnix, Michael C. & Schäfer, Rudi, 2011. "A copula approach on the dynamics of statistical dependencies in the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4251-4259.
    8. 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.
    9. Alexander F. R. Koivusalo & Rudi Schafer, 2011. "Calibration of structural and reduced-form recovery models," Papers 1102.4864, arXiv.org.

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