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A reinforced urn process modeling of recovery rates and recovery times

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  • Cheng, Dan
  • Cirillo, Pasquale
Abstract
Answering a major demand in modern credit risk management, we propose a nonparametric survival approach for the modeling of the recovery rate and the recovery time of a defaulted counterparty, by introducing what we call the Recovery Reinforced Urn Process, a special type of combinatorial stochastic process.

Suggested Citation

  • Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
    • Handle: RePEc:eee:jbfina:v:96:y:2018:i:c:p:1-17
    DOI: 10.1016/j.jbankfin.2018.08.014
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    References listed on IDEAS

    as
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    Cited by:

    1. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    2. 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.
    3. Souto Arias, Luis A. & Cirillo, Pasquale, 2021. "Joint and survivor annuity valuation with a bivariate reinforced urn process," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 174-189.
    4. Tarun Chitra & Alex Evans, 2020. "Why Stake When You Can Borrow?," Papers 2006.11156, arXiv.org.
    5. Rubén García-Céspedes & Manuel Moreno, 2020. "Random LGD adjustments in the Vasicek credit risk model," The European Journal of Finance, Taylor & Francis Journals, vol. 26(18), pages 1856-1875, December.
    6. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.

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