Analyzing the effect of business cycle on rating transitions has been a subject of great interest these last fifteen years, particularly due to the increasing pressure coming from regulators for stress testing. In this paper, we consider that the dynamics of rating migrations is governed by an unobserved latent factor. Under a point process filtering framework, we explain how the current state of the hidden factor can be efficiently inferred from observations of rating histories. We then adapt the classical Baum-Welsh algorithm to our setting and show how to estimate the latent factor parameters. Once calibrated, we may reveal and detect economic changes affecting the dynamics of rating migration, in real-time. To this end we adapt a filtering formula which can then be used for predicting future transition probabilities according to economic regimes without using any external covariates. We propose two filtering frameworks: a discrete and a continuous version. We demonstrate and compare the efficiency of both approaches on fictive data and on a corporate credit rating database. The methods could also be applied to retail credit loans.

翻译：分析商业周期对评级迁移的影响是过去十五年来备受关注的课题，尤其是在监管机构对压力测试要求日益增强的背景下。本文认为评级迁移的动态过程由未观测到的潜在因子所主导。在点过程过滤框架下，我们阐释如何根据评级历史观测数据有效推断隐含因子的当前状态。随后，我们将经典的Baum-Welsh算法适配至本文场景，并展示潜在因子参数的估计方法。完成参数校准后，即可实时揭示并检测影响评级迁移动态的经济变化。为此，我们适配了一种过滤公式，该公式无需外部协变量即可根据经济状态预测未来迁移概率。我们提出两种过滤框架：离散版本与连续版本。通过虚构数据与企业信用评级数据库，对两种方法的有效性进行验证与比较。该分析方法同样适用于零售信用贷款领域。