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Downward migration credit risk problem: a non-homogeneous backward semi-Markov reliability approach

Author

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  • Guglielmo D’Amico

    (Università ‘G. d’Annunzio’ di Chieti, Chieti, Italy)

  • Jacques Janssen

    (Universitè Libre de Bruxelles, Brest, Belgium)

  • Raimondo Manca

    (Università ‘La Sapienza’, Roma, Italy)

Abstract

International organizations evaluate credit risk and rank firms according to risk by assigning them a ‘rating’. The time evolution of a rating can be studied by means of Markov models. Some papers have outlined the problem pertaining to the unsuitable fitting of Markov processes in a credit risk environment. This paper presents a model that overcomes the problems given by the Markov rating models. It includes non-homogeneity, the downward problem and the randomness of time in the transitions of states, thus making it possible to consider the duration inside a state in a complete way. In this paper, both, the transient and asymptotic analyses are presented. The asymptotic analysis is performed by using a mono-unireducible topological structure. Moreover, a real data application is conducted using the historical database of Standard & Poor’s as the source.

Suggested Citation

  • Guglielmo D’Amico & Jacques Janssen & Raimondo Manca, 2016. "Downward migration credit risk problem: a non-homogeneous backward semi-Markov reliability approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(3), pages 393-401, March.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:3:p:393-401
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    Citations

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    Cited by:

    1. Vlad Stefan Barbu & Guglielmo D’Amico & Thomas Gkelsinis, 2021. "Sequential Interval Reliability for Discrete-Time Homogeneous Semi-Markov Repairable Systems," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
    2. 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.
    3. D’Amico, Guglielmo & Gismondi, Fulvio & Petroni, Filippo & Prattico, Flavio, 2019. "Stock market daily volatility and information measures of predictability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 22-29.
    4. 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.
    5. Guglielmo D’Amico & Thomas Gkelsinis, 2024. "On a Mixed Transient–Asymptotic Result for the Sequential Interval Reliability for Semi-Markov Chains," Mathematics, MDPI, vol. 12(12), pages 1-18, June.
    6. P.-C.G. Vassiliou, 2020. "Non-Homogeneous Semi-Markov and Markov Renewal Processes and Change of Measure in Credit Risk," Mathematics, MDPI, vol. 9(1), pages 1-27, December.
    7. Riccardo De Blasis, 2020. "The price leadership share: a new measure of price discovery in financial markets," Annals of Finance, Springer, vol. 16(3), pages 381-405, September.
    8. Camilla Ferretti & Giampaolo Gabbi & Piero Ganugi & Federica Sist & Pietro Vozzella, 2019. "Credit Risk Migration and Economic Cycles," Risks, MDPI, vol. 7(4), pages 1-18, October.
    9. Puneet Pasricha & Dharmaraja Selvamuthu, 2021. "A Markov regenerative process with recurrence time and its application," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-22, December.
    10. D’Amico, Guglielmo & Scocchera, Stefania & Storchi, Loriano, 2018. "Financial risk distribution in European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 252-267.
    11. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.

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