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Underperforming performance measures? A review of measures for loss given default models
Journal of Risk Model Validation ( IF 0.250 ) Pub Date : 2018-01-01 , DOI: 10.21314/jrmv.2018.186
Katarzyna Bijak , Lyn C. Thomas

As far as Probability of Default (PD) prediction is concerned, the model performance is typically measured with the Gini coefficient and/or the Kolmogorov-Smirnov (KS) statistic. For Loss Given Default (LGD) models, there are no standard performance measures, though, and more than 15 different measures are used, including Mean Square Error (MSE), Mean Absolute Error (MAE), coefficient of determination (R-squared) as well as correlation coefficients between the observed and predicted LGD. However, some measures cannot be readily recommended for LGD models, even though they have been used for this purpose. It is argued that there are measures that should only be employed for specific types of models. It is also pointed out that some measures can be applied interchangeably to avoid information redundancy. Moreover, the Area Under the Receiver Operating Characteristic Curve (AUC) is critically discussed in the LGD context. Four new measures are then proposed: Mean Area Under the Receiver Operating Characteristic Curve (MAUROC), Mean Accuracy Ratio (MAR), Mean Enhanced Lin-Lin Error (MELLE) and a generalized lift. The review is illustrated using an empirical example.

中文翻译:

绩效指标不佳?审查给定默认模型下的损失计量

就默认概率(PD)预测而言,通常使用基尼系数和/或Kolmogorov-Smirnov(KS)统计量来衡量模型性能。但是,对于默认违约损失(LGD)模型,没有标准的性能指标,并且使用了15种以上的指标,包括均方误差(MSE),平均绝对误差(MAE),确定系数(R平方)。以及观察到的和预测的LGD之间的相关系数。但是,即使已将某些措施用于LGD模型,也不能轻易推荐这些措施。有人认为,有些措施只应用于特定类型的模型。还指出,一些措施可以互换地应用以避免信息冗余。此外,在LGD环境中严格讨论了接收器工作特性曲线(AUC)下的面积。然后提出了四个新的度量:接收器工作特性曲线下的平均面积(MAUROC),平均准确率(MAR),平均增强的线性误差(MELLE)和广义升程。使用一个经验示例来说明该评论。
更新日期:2018-01-01
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