论文标题
联合默认评估中的机器学习技术
Machine learning techniques in joint default assessment
论文作者
论文摘要
本文研究了捕获非线性依赖的后果，这些协变量驱动了不同债务人的违约和信用组合的整体风险。联合默认建模是，没有一般性的伯努利混合模型。使用对信用卡数据集的应用程序，我们表明，即使机器学习技术的性能仅比logistic回归略好，而在将单个默认值分类为协变量的函数时，它们也确实在投资组合级别上表现优于它。发生这种情况是因为它们捕获了协变量之间的线性和非线性依赖性，而逻辑回归仅捕获线性依赖性。与逻辑回归相比，机器学习方法在协变量中捕获非线性依赖性的能力会产生更高的默认相关性。结果，在我们的数据上，逻辑回归低估了信用组合的风险。
This paper studies the consequences of capturing non-linear dependence among the covariates that drive the default of different obligors and the overall riskiness of their credit portfolio. Joint default modeling is, without loss of generality, the classical Bernoulli mixture model. Using an application to a credit card dataset we show that, even when Machine Learning techniques perform only slightly better than Logistic Regression in classifying individual defaults as a function of the covariates, they do outperform it at the portfolio level. This happens because they capture linear and non-linear dependence among the covariates, whereas Logistic Regression only captures linear dependence. The ability of Machine Learning methods to capture non-linear dependence among the covariates produces higher default correlation compared with Logistic Regression. As a consequence, on our data, Logistic Regression underestimates the riskiness of the credit portfolio.
