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.
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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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Boreiko, D.V., Kaniovski, Y.M., Pflug, G.C. (2017). Modeling of Dependent Credit Rating Transitions Governed by Industry-Specific Markovian Matrices. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_71
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DOI: https://doi.org/10.1007/978-3-319-42902-1_71
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