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Long-range Ising model for credit portfolios with heterogeneous credit exposures

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  • Kato, Kensuke

Abstract

We propose the finite-size long-range Ising model as a model for heterogeneous credit portfolios held by a financial institution in the view of econophysics. The model expresses the heterogeneity of the default probability and the default correlation by dividing a credit portfolio into multiple sectors characterized by credit rating and industry. The model also expresses the heterogeneity of the credit exposure, which is difficult to evaluate analytically, by applying the replica exchange Monte Carlo method to numerically calculate the loss distribution. To analyze the characteristics of the loss distribution for credit portfolios with heterogeneous credit exposures, we apply this model to various credit portfolios and evaluate credit risk. As a result, we show that the tail of the loss distribution calculated by this model has characteristics that are different from the tail of the loss distribution of the standard models used in credit risk modeling. We also show that there is a possibility of different evaluations of credit risk according to the pattern of heterogeneity.

Suggested Citation

  • Kato, Kensuke, 2016. "Long-range Ising model for credit portfolios with heterogeneous credit exposures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1103-1119.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:1103-1119
    DOI: 10.1016/j.physa.2016.06.127
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    References listed on IDEAS

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    1. J. Molins & E. Vives, 2015. "Model risk on credit risk," Papers 1502.06984, arXiv.org, revised Dec 2015.
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    4. 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.
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    1. Kensuke Kato & Nobuhiro Nakamura, 2024. "PDE-Based Bayesian Inference of CEV Dynamics for Credit Risk in Stock Prices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(2), pages 389-421, June.

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