Numerical estimates of risk factors contingent on credit ratings

T. Gärtner (), S. Kaniovski () and Y. Kaniovski ()
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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
Authors registered in the RePEc Author Service: Yuri (Yuriy) Kaniovski (Kaniovskyi), ()

Computational Management Science, 2021, vol. 18, issue 4, No 6, 563-589

Abstract: 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.

Keywords: Macroeconomic scenario; Combinatorial complexity; Maximum likelihood; Random search; Genetic algorithm; Penalty; 90C30; 90C90 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10287-021-00405-9

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