Identification of hidden Markov chains governing dependent credit-rating migrations
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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 idiosyncratic and a common component. The larger is the pool of debtors affected by the same common component, the stronger is the dependence among migrations. The distribution of the common component depends on macroeconomic conditions. At every time instant, the resulting allocation of debtors to credit classes and industries follows a mixture of multinomial distributions. Dealing with M non default credit classes, there are 2 M theoretically possible macroeconomic outcomes. Only few of them occur with a positive probability. Restricting the macroeconomic dynamics to such outcomes simplifies estimation. A heuristics for identifying them is suggested. Using the maximum likelihood method, it was tested on a Standard and Poor's (S&P's) data set.
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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 D; Kaniovski S; Kaniovski Y; Pflug G (2017)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 ...