Statistical Surveillance of Structural Breaks in Credit Rating Dynamics.

Haipeng Xing,Ke Wang,Zhi Li,Ying Chen

doi:10.3390/e22101072

Abstract

The 2007–2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms’ credit rating transition dynamics as a proxy of the credit market and discuss how statistical process control tools can be used to surveil structural breaks in firms’ rating transition dynamics. After reviewing some commonly used Markovian models for firms’ rating transition dynamics, we present several surveillance rules for detecting changes in generators of firms’ rating migration matrices, including the likelihood ratio rule, the generalized likelihood ratio rule, the extended Shiryaev’s detection rule, and a Bayesian detection rule for piecewise homogeneous Markovian models. The effectiveness of these rules was analyzed on the basis of Monte Carlo simulations. We also provide a real example that used the surveillance rules to analyze and detect structural breaks in the monthly credit rating migration of U.S. firms from January 1986 to February 2017.

Highlights

The 2007–2008 global financial crisis, originally triggered by the U.S subprime mortgage crisis in 2007 and quickly spread over the U.S and the rest of the world via the U.S and international banking systems, caused severe economic, political, and social consequences over the world
As general properties of these surveillance rules have been discussed in the literature, we focus on the effectiveness of these rules for detecting changes in firms’ rating transition dynamics
This paper studied the problem of the sequential surveillance of structural breaks in firms’ rating transition dynamics

Summary

Introduction

The 2007–2008 global financial crisis, originally triggered by the U.S subprime mortgage crisis in 2007 and quickly spread over the U.S and the rest of the world via the U.S and international banking systems, caused severe economic, political, and social consequences over the world. March 2020 placed the global financial system under further strain and triggered a global economic downturn [1]. These events raise some important questions for economists, financial practitioners, and government regulators, and one of them is whether quantitative tools can be developed to surveil the occurrence of crises or structural breaks in the credit market or its sub-markets.

Methods

Results

Conclusion