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Credit Risk in an Economy with New Firms Arrivals

Author

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  • Silvia Centanni

    (University of Verona)

  • Immacolata Oliva

    (University of Verona)

  • Paola Tardelli

    (University of L’Aquila)

Abstract

We propose a dynamic model to analyze the credit quality of firms. In the market in which they operate, the firms are divided into a finite number of classes representing their credit status. The cardinality of the population can increase, since new firms can enter the market and the partition is supposed to change over time, due to defaults and changes in credit quality, following a class of Markov processes. Some conditional probabilities related to default times are investigated and the role of occupation numbers is highlighted in this context. In a partial information setting at discrete time, we present a particle filtering technique to numerically compute by simulation the conditional distribution of the number of firms in the credit classes, given the information up to time t.

Suggested Citation

  • Silvia Centanni & Immacolata Oliva & Paola Tardelli, 2017. "Credit Risk in an Economy with New Firms Arrivals," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 891-912, September.
    • Handle: RePEc:spr:metcap:v:19:y:2017:i:3:d:10.1007_s11009-016-9525-4
    DOI: 10.1007/s11009-016-9525-4
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    References listed on IDEAS

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    1. Crouhy, Michel & Galai, Dan & Mark, Robert, 2001. "Prototype risk rating system," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 47-95, January.
    2. Farnsworth, Heber & Li, Tao, 2007. "The Dynamics of Credit Spreads and Ratings Migrations," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 595-620, September.
    3. West, Rr, 1970. "Alternative Approach To Predicting Corporate Bond Ratings," Journal of Accounting Research, Wiley Blackwell, vol. 8(1), pages 118-125.
    4. Alexander Herbertsson, 2011. "Modelling default contagion using multivariate phase-type distributions," Review of Derivatives Research, Springer, vol. 14(1), pages 1-36, April.
    5. Feng, D. & Gourieroux, C. & Jasiak, J., 2008. "The ordered qualitative model for credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 111-130, January.
    6. Tomas Björk & Mark Davis & Camilla Landén, 2010. "Optimal investment under partial information," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 71(2), pages 371-399, April.
    7. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
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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. Paulo V. Carvalho & José D. Curto & Rodrigo Primor, 2022. "Macroeconomic determinants of credit risk: Evidence from the Eurozone," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2054-2072, April.

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