A new approach to detecting change in credit quality

Rusudan Kevkhishvili

Abstract

This paper provides a new framework for the detection and quantification of change in the corporate credit quality of established companies. These companies are important players in the economy, and analysis of their creditworthiness is of great interest to investors. Our approach is based on the observation that the deterioration of credit quality in established companies is a long-term process, because these firms use their resources as well as strong customer and supplier relationships to respond to market and technological changes. Based solely on the realized path of the stochastic process that represents creditworthiness, we propose an economically plausible endogenous mechanism that governs the shift in the long-term mean of the process. Specifically, we construct a model that detects a change in the mean-reversion level of an Ornstein–Uhlenbeck process representing the company’s leverage. Our model captures this change based on a number of realized upcrossings of a certain level (estimated from data) by the process itself, without introducing an additional source of uncertainty. Our approach is computationally simple and provides an efficient tool for monitoring changes in quantities related to credit quality, such as default probability.