Abstract
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.
Highlights
Data Availability Statement: All relevant data are within the paper and its Supporting Information files
The macroeconomic factors are modeled as credit-class-specific, their impact is not diversified across industries and they do not evolve in time
While entries of Q are very close to their counterparts estimated without the additional requirement for the block structure, they do not match well the values reported for the models, where macroeconomic factors are not differentiated with industry-specific tendency variables
Summary
Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Having a distribution ~p, coefficients of correlation between hidden variables can be evaluated They may be interpreted in terms of macroeconomic factors affecting debtors belonging to the corresponding credit classes. Since model 1 requires the same common component for all debtors belonging to a credit class, this parametrization cannot be implemented with industry-specific tendency variables. (For a larger threshold of 0.01, in both cases there are 11 realizations and the corresponding probabilities are 1.0000 and 0.9831.) Probability of the event that all tendency variables attain the value 1 is pð11Þ 1⁄4 0:5720 and pð12Þ 1⁄4 0:5661, correspondingly Interpreting conceptually these values, we conclude that according to both models approximately 57% of years from the period between 1991 and 2015 were favorable for all debtors rated by the S&P’s. The first of the matrices contains below the main diagonal above it
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