Numerical estimates of risk factors contingent on credit ratings
| Autor: | Timon Gärtner, Serguei Kaniovski, Yuriy Kaniovski |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
040101 forestry
Class (set theory) 021103 operations research business.industry Financial risk 0211 other engineering and technologies Distribution (economics) 04 agricultural and veterinary sciences 02 engineering and technology Maximization Management Information Systems Credit rating Random search Genetic algorithm Econometrics Economics 0401 agriculture forestry and fisheries Likelihood function business Information Systems |
| Zdroj: | Computational Management Science. 18:563-589 |
| ISSN: | 1619-6988 1619-697X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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