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

  • Dmitri V. Boreiko
  • Yuri M. KaniovskiEmail author
  • Georg Ch. Pflug
Conference paper
Part of the Operations Research Proceedings book series (ORP)

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.

Keywords

Credit Rating Common Component Industry Sector Markov Chain Model Coupling Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Financial support from the Free University of Bozen-Bolzano for the project “Coupled Markov chains models for evaluating credit and systemic risk” is gratefully acknowledged.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Dmitri V. Boreiko
    • 1
  • Yuri M. Kaniovski
    • 1
    Email author
  • Georg Ch. Pflug
    • 2
  1. 1.Faculty of Economics and ManagementFree University of Bozen-BolzanoBolzanoItaly
  2. 2.Department of Statistics and Decision Support SystemsUniversity of ViennaViennaAustria

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