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Loss given default models incorporating macroeconomic variables for credit cards

机译:给定默认模型的损失,其中包含信用卡的宏观经济变量

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Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to model Loss Given Default in downturn conditions, as required by Basel II, and enables stress testing. We find that bank interest rates and the unemployment level significantly affect LGD.
机译:基于主要零售信用卡的英国数据,我们基于帐户级别数据构建了多种违约损失模型,包括Tobit,决策树模型,Beta和部分logit转换。我们发现,具有宏观经济变量的普通最小二乘模型在独立保留数据集的帐户和投资组合级别上的预测违约损失表现最佳。在模型中纳入宏观经济条件很重要,因为它提供了一种方法,可以根据巴塞尔协议II的要求,在经济低迷的情况下对违约损失进行建模,并可以进行压力测试。我们发现,银行利率和失业水平对LGD有很大影响。

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