Are credit ratings time-homogeneous and Markov?

TL;DR

This paper introduces a method to test whether credit rating processes are time-homogeneous and Markovian, revealing that these assumptions often do not hold in real-world data, impacting risk assessment reliability.

Contribution

The paper presents a simple testing approach for the Markov and homogeneity assumptions in credit rating time series, using empirical data from Moody's.

Findings

Homogeneity and Markov assumptions are not always valid in credit rating data.

The proposed method helps quantify confidence in credit risk predictions.

Empirical analysis shows deviations from theoretical assumptions in real data.

Abstract

We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody's and show that the validity of these assumptions - existence of a homogeneous generator and Markovianity - is not always guaranteed. Our analysis is based on a comparison between empirical transition matrices aggregated over fixed time windows and candidate transition matrices generated from measurements taken over shorter periods. Ratings are widely used in credit risk, and are a key element in risk assessment; our results provide a tool for quantifying confidence in predictions extrapolated from these time series.