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Numerical estimates of risk factors contingent on credit ratings

Author

Listed:
  • T. Gärtner

    (Provincial Statistics Institute (ASTAT), Autonomous Province of Bolzano-South Tyrol)

  • S. Kaniovski

    (Austrian Institute for Economic Research (WIFO))

  • Y. Kaniovski

    (Free University of Bozen-Bolzano)

Abstract

Assuming a favorable or an adverse outcome for every combination of a credit class and an industry sector, a binary string, termed as a macroeconomic scenario, is considered. Given historical transition counts and a model for dependence among credit-rating migrations, a probability is assigned to each of the scenarios by maximizing a likelihood function. Applications of this distribution in financial risk analysis are suggested. Two classifications are considered: 7 non-default credit classes with 6 industry sectors and 2 non-default credit classes with 12 industry sectors. We propose a heuristic algorithm for solving the corresponding maximization problems of combinatorial complexity. Probabilities and correlations characterizing riskiness of random events involving several industry sectors and credit classes are reported.

Suggested Citation

  • T. Gärtner & S. Kaniovski & Y. Kaniovski, 2021. "Numerical estimates of risk factors contingent on credit ratings," Computational Management Science, Springer, vol. 18(4), pages 563-589, October.
    • Handle: RePEc:spr:comgts:v:18:y:2021:i:4:d:10.1007_s10287-021-00405-9
    DOI: 10.1007/s10287-021-00405-9
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