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Predicting loss given default in leasing: A closer look at models and variable selection

机译：预测租赁中默认的损失：仔细研究模型和变量选择

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摘要

Since the introduction of the Basel II Accord, and given its huge implications for credit risk management, the modeling and prediction of the loss given default (LGD) have become increasingly important tasks. Institutions which use their own LGD estimates can build either simpler or more complex methods. Simpler methods are easier to implement and more interpretable, but more complex methods promise higher prediction accuracies. Using a proprietary data set of 1,184 defaulted corporate leases in Germany, this study explores different parametric, semi-parametric and non-parametric approaches that attempt to predict the LGD. By conducting the analyses for different information sets, we study how the prediction accuracy changes depending on the set of information that is available. Furthermore, we use a variable importance measure to identify the input variables that have the greatest effects on the LGD prediction accuracy for each method. In this regard, we provide new insights on the characteristics of leasing LGDs. We find that (1) more sophisticated methods, especially the random forest, lead to remarkable increases in the prediction accuracy; (2) updating information improves the prediction accuracy considerably; and (3) the outstanding exposure at default, an internal rating, asset types and lessor industries turn out to be important drivers of accurate LGD predictions. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：自引入《巴塞尔协议II》以来，鉴于其对信用风险管理的巨大影响，违约损失（LGD）的建模和预测已变得越来越重要。使用自己的LGD估算值的机构可以建立更简单或更复杂的方法。更简单的方法更易于实现和解释，但是更复杂的方法可保证更高的预测准确性。本研究使用德国1,184个违约企业租赁的专有数据集，探索了试图预测LGD的不同参数，半参数和非参数方法。通过对不同信息集进行分析，我们研究了预测准确性如何根据可用信息集而变化。此外，对于每种方法，我们使用变量重要性度量来识别对LGD预测精度影响最大的输入变量。在这方面，我们提供了关于租赁违约损失率特征的新见解。我们发现（1）更复杂的方法，尤其是随机森林，导致预测准确性显着提高; （2）更新信息大大提高了预测准确性; （3）拖欠的违约风险敞口，内部评级，资产类型和出租行业证明是准确LGD预测的重要驱动力。 （C）2019国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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