Modeling of Dependent Credit Rating Transitions Governed by Industry-Specific Markovian Matrices
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.
KeywordsCredit Rating Common Component Industry Sector Markov Chain Model Coupling Scheme
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.
- 2.Boreiko, D.V., Kaniovski, Y.M., Pflug, G.Ch.: Modeling dependent credit rating transitions - a comparison of coupling schemes and empirical evidence. Central Eur. J. Oper. Res. (2015). doi: 10.1007/s10100-015-0415-6
- 3.Gupton, G.M., Finger, Ch.C., Bhatia, M.: Credit Metrics – Technical Document. Technical report, J.P. Morgan Inc. (1997)Google Scholar
- 4.Kaniovski, Y.M., Pflug, G.Ch.: Risk assessment for credit portfolios: a coupled Markov chain model. J. Bank. Financ. 31, 2303–2323 (2007)Google Scholar
- 5.Nagpal, K., Bahar, R.: Measuring default correlation. Risk 14, 129–132 (2001)Google Scholar