Dependent credit-rating migrations: coupling schemes, estimators and simulation
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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 or adverse, that are encoded by the corresponding (unobserved) tendency variables as $1$ and $0$. Computational resources required for estimation of such mixtures depend upon the pattern of tendency variables. Unlike in the known coupling schemes, the credit-class-specific tendency variables considered here can evolve as a (hidden) time-homogeneous Markov chain. In order to identify unknown parameters of the corresponding mixtures of multinomial distributions, maximum likelihood estimators are suggested and tested on a Standard and Poor's dataset using MATLAB optimization software.
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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 ...
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 ...