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An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages

Author

Listed:
  • Dan Cheng

    (Applied Probability Group, Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, 2628 XE Delft, The Netherlands
    These authors contributed equally to this work.)

  • Pasquale Cirillo

    (Applied Probability Group, Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, 2628 XE Delft, The Netherlands
    These authors contributed equally to this work.
    Current address: E1.260 Building 28, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands.)

Abstract

We propose an alternative approach to the modeling of the positive dependence between the probability of default and the loss given default in a portfolio of exposures, using a bivariate urn process. The model combines the power of Bayesian nonparametrics and statistical learning, allowing for the elicitation and the exploitation of experts’ judgements, and for the constant update of this information over time, every time new data are available. A real-world application on mortgages is described using the Single Family Loan-Level Dataset by Freddie Mac.

Suggested Citation

  • Dan Cheng & Pasquale Cirillo, 2019. "An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages," Risks, MDPI, vol. 7(3), pages 1-21, July.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:3:p:76-:d:246367
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    References listed on IDEAS

    as
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    Cited by:

    1. Christoph Frei, 2020. "A New Approach to Risk Attribution and Its Application in Credit Risk Analysis," Risks, MDPI, vol. 8(2), pages 1-13, June.
    2. James A. Kahn & Benjamin S. Kay, 2024. "The Impact of Credit Risk Mispricing on Mortgage Lending during the Subprime Boom," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1021-1052, August.

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