Traces of business cycles in credit-rating migrations
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Using migration data of a rating agency, this paper attempts to quantify the impact of macroeconomic conditions on credit-rating migrations. The migrations are modeled as a coupled Markov chain, where the macroeconomic factors are represented by unobserved tendency variables. In the simplest case, these binary random variables are static and credit-class-specific. A generalization treats tendency variables evolving as a time-homogeneous Markov chain. A more detailed analysis assumes a tendency variable for every combination of a credit class and an industry. The models are tested on a Standard and Poor’s (S&P’s) dataset. Parameters are estimated by the maximum likelihood method. According to the estimates, the investment-grade financial institutions evolve independently of the rest of the economy represented by the data. This might be an evidence of implicit too-big-to-fail bail-out guarantee policies of the regulatory authorities.
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Boreiko D; Kaniovski S; Kaniovski Y; Pflug G (ISAST: International Society for the Advancement of Science and Technology, 2016)By mixing an idiosyncratic component with a common one, coupling schemes allow to model dependent credit-rating migrations. The distribution of the common component is modified according to macroeconomic conditions, favorable ...
Boreiko D; Kaniovski Y; Pflug GC (2016)Two models of dependent credit rating migrations governed by industry-specific Markovian matrices, are considered. Caused by macroeconomic factors, positive and negative unobserved tendencies, encoded as values “1” or “0” ...
Modeling dependent credit rating transitions: a comparison of coupling schemes and empirical evidence Boreiko D; Kaniovski Y; Pflug GC (2015)Three coupling schemes for generating dependent credit rating transitions are compared and empirically tested. Their distributions, the corresponding variances and default correlations are characterized. Using Standard and ...