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 GC (2018)To quantify the impact of business cycles on the dynamics of credit ratings, conditional migration matrices and probabilities of the corresponding macroeconomic scenarios are estimated. The approach is tested on a Standard ...
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 DV; Kaniovski SY; Kaniovski YM; Pflug G (2019)Three models of dependent credit-rating migrations are considered. Each of them entails a coupling scheme and a discrete-time Markovian macroeconomic dynamics. Every credit-rating migration is modeled as a mixture of an ...