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Modeling of Dependent Credit Rating Transitions Governed by Industry-Specific Markovian Matrices

In: Operations Research Proceedings 2015

Author

Listed:
  • Dmitri V. Boreiko

    (Free University of Bozen-Bolzano)

  • Yuri M. Kaniovski

    (Free University of Bozen-Bolzano)

  • Georg Ch. Pflug

    (University of Vienna)

Abstract

Two coupling schemes where probabilities of credit rating migrations vary across industry sectors are introduced. Favorable and adverse macroeconomic factors, encoded as values 1 and 0, of credit class- and industry-specific unobserved tendency variables, modify the transition probabilities rendering individual evolutions dependent. Unlike in the known coupling schemes, expansion in some industry sectors and credit classes coexists with shrinkage in the rest. The schemes are tested on Standard and Poor’s data. Maximum likelihood estimators and MATLAB optimization software were used.

Suggested Citation

  • Dmitri V. Boreiko & Yuri M. Kaniovski & Georg Ch. Pflug, 2017. "Modeling of Dependent Credit Rating Transitions Governed by Industry-Specific Markovian Matrices," Operations Research Proceedings, in: Karl Franz Dörner & Ivana Ljubic & Georg Pflug & Gernot Tragler (ed.), Operations Research Proceedings 2015, pages 525-531, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-42902-1_71
    DOI: 10.1007/978-3-319-42902-1_71
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