压力测试中违约概率的预测：模型比较
Predicting Default Probabilities for Stress Tests: A Comparison of Models
摘要：
自金融危机以来，压力测试作为评估金融机构对不利金融和经济发展的适应能力的工具的使用大大增加。这类工作的一个关键部分是使用宏观金融联系模型将宏观经济变量转换为信贷风险的违约概率。对这些模型的一个关键要求是，它们应该能够结合大多数短数据样本，从广泛的宏观经济变量中正确地检测出信号。本文的目的是比较大量不同的回归模型，以找到最好的表现信用风险模型。我们建立了一个评估框架，使我们能够在同一环境中系统地评估和评估大量模型。我们的研究结果表明，确实有更好的性能模型比目前的国家的最先进的模型。此外，我们的比较揭示了其他潜在的信用风险模型，特别强调了机器学习模型和预测组合的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
