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Interacting default intensity with a hidden Markov process

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  • Feng-Hui Yu
  • Wai-Ki Ching
  • Jia-Wen Gu
  • Tak-Kuen Siu

Abstract

In this paper we consider a reduced-form intensity-based credit risk model with a hidden Markov state process. A filtering method is proposed for extracting the underlying state given the observation processes. The method can be applied to a wide range of problems. Based on this model, we derive the joint distribution of multiple default times without imposing stringent assumptions on the form of default intensities. Closed-form formulas for the distribution of default times are obtained which are then applied to solve a number of practical problems such as hedging and pricing credit derivatives. The method and numerical algorithms presented can be applicable to various forms of default intensities.

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  • Feng-Hui Yu & Wai-Ki Ching & Jia-Wen Gu & Tak-Kuen Siu, 2017. "Interacting default intensity with a hidden Markov process," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 781-794, May.
    • Handle: RePEc:taf:quantf:v:17:y:2017:i:5:p:781-794
    DOI: 10.1080/14697688.2016.1237036
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    Cited by:

    1. Puneet Pasricha & Dharmaraja Selvamuthu & Guglielmo D’Amico & Raimondo Manca, 2020. "Portfolio optimization of credit risky bonds: a semi-Markov process approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-14, December.
    2. Feng-Hui Yu & Jiejun Lu & Jia-Wen Gu & Wai-Ki Ching, 2019. "Modeling Credit Risk with Hidden Markov Default Intensity," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1213-1229, October.

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