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Creditworthiness dynamics and Hidden Markov Models

Author

Listed:
  • L Quirini

    (Consum.it—Monte dei Paschi di Siena Group, Calenzano, Italy)

  • L Vannucci

    (University of Florence, Florence, Italy)

Abstract

A dynamic monitoring of credit risky portfolios is described. In the first section, it is shown how a Markov dependence can be used in modelling the borrower's behaviour: a chain of transition probabilities matrices is built in which the states of the dynamic stochastic system are the number of instalments in arrears. In the second part, such a model is generalized in the framework of the Hidden Markov Models to explain how the credit market conditions could affect the borrower's payment process. Numerical examples complete the note.

Suggested Citation

  • L Quirini & L Vannucci, 2014. "Creditworthiness dynamics and Hidden Markov Models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 323-330, March.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:3:p:323-330
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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. Ntwiga, Davis Bundi, 2018. "Credit risk analysis for low income earners," KBA Centre for Research on Financial Markets and Policy Working Paper Series 24, Kenya Bankers Association (KBA).
    3. Galina A. Timofeeva & Yana A. Bozhalkina, 2018. "Dependence of a Loan Portfolio Structure on a Cut-Off Level in a Scoring Model," Journal of New Economy, Ural State University of Economics, vol. 19(2), pages 24-35, April.
    4. Jonathan Crook & David Edelman, 2014. "Special issue credit risk modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 321-322, March.

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