论文标题
预测压力测试的默认概率：模型的比较
Predicting Default Probabilities for Stress Tests: A Comparison of Models
论文作者
论文摘要
自从大金融危机（GFC）以来，使用压力测试作为评估金融机构对不利金融和经济发展的弹性的一种工具，已大大增加。此类练习中的一个关键部分是通过使用大型财务联系模型将宏观经济变量转换为默认的信用风险概率。这种模型的关键要求是，他们应该能够从多种宏观经济变量与大多数短数据样本结合使用多种宏观经济变量。本文的目的是比较大量不同的回归模型，以找到最佳性能的信用风险模型。我们设置了一个估算框架，使我们能够系统地估算并评估在同一环境中的大量模型。我们的结果表明，确实比当前的最新模型更好地表现模型。此外，我们的比较阐明了其他潜在的信用风险模型，特别是强调了机器学习模型和预测组合的优势。
Since the Great Financial Crisis (GFC), the use of stress tests as a tool for assessing the resilience of financial institutions to adverse financial and economic developments has increased significantly. One key part in such exercises is the translation of macroeconomic variables into default probabilities for credit risk by using macrofinancial linkage models. A key requirement for such models is that they should be able to properly detect signals from a wide array of macroeconomic variables in combination with a mostly short data sample. The aim of this paper is to compare a great number of different regression models to find the best performing credit risk model. We set up an estimation framework that allows us to systematically estimate and evaluate a large set of models within the same environment. Our results indicate that there are indeed better performing models than the current state-of-the-art model. Moreover, our comparison sheds light on other potential credit risk models, specifically highlighting the advantages of machine learning models and forecast combinations.
