Back-testing credit risk parameters on low default portfolios: a simple Bayesian transfer learning approach with an application to sovereign risk‖
针对主权等低违约组合中违约数据极少的问题，提出一种基于贝叶斯迁移学习的模型，利用预期违约频率和实际违约数据来估计违约概率，并通过模拟验证其优于机器学习方法，易于实施且避免过拟合。
The estimation of Probabilities of Default (PD) is particularly challenging in the context of low-default portfolios. For example, Sovereign portfolios often exhibit very few (or even zero) defaults, making frequentist approaches impractical. Motivated by these considerations, we propose a model based on a simple Bayesian transfer learning approach depending on Expected Default Frequencies (EDF) and observed defaults. The model is founded on a sound statistical methodology, ensuring meaningful risk differentiation and accurate, consistent estimates, with PDs that are strictly monotonic as creditworthiness decreases. In a simulation analysis, we compared the results of this approach with those obtained using transfer learning implemented through a machine learning algorithm. The advantage of the Bayesian model lies in its ease of implementation and interpretation, as well as its ability to ‘automatically’ balance the relevance attributed to observed defaults and the Expected Default Frequencies used as a proxy, without the risk of overfitting.