This is the first paper to propose a flexible local logit regression for defaulted loan recoveries that lie in [0,1]. Via a simulation study, we demonstrate that the proposed model is robust to nonlinearity, and non-normality of errors. Applied to Moody’s dataset, the local logit model uncovers the intrinsic nonlinear relationship between loan recoveries and covariates, which include loan/borrower characteristics and economic conditions. We exploit the empirical features of the local logit model to improve the specification of the standard regression for the fractional response variable (RFRV) model, which we refer to as the calibrated-RFRV model. The estimation of the calibrated-RFRV model is more straightforward and faster than the local logit model. The overall out-of-sample predictive performance of the calibrated-RFRV is superior to the local logit, RFRV, neural network (NN), regression tree (RT) and Inverse Gaussian (IG) models. The local logit model outperforms others in quantile forecasting, showing the attractiveness of this model for estimating tail risks, the accurate estimation of which is beneficial to risk managers.

这是第一篇提出针对[0,1]的拖欠贷款回收进行灵活的本地logit回归的论文。通过仿真研究，我们证明了所提出的模型对非线性和误差的非正态性具有鲁棒性。将本地logit模型应用于Moody的数据集后，它揭示了贷款回收率与协变量之间的内在非线性关系，其中包括贷款/借款人的特征和经济状况。我们利用局部logit模型的经验特征来改进分数响应变量（RFRV）模型的标准回归的规范，该模型称为校准RFRV模型。校正后的RFRV模型的估计比本地logit模型的估计更直接，更快捷。校正后的RFRV的整体样本外预测性能优于本地logit，RFRV，神经网络（NN），回归树（RT）和逆高斯（IG）模型。局部logit模型在分位数预测中胜过其他模型，显示了该模型在估算尾部风险方面的吸引力，其准确估计对风险管理者是有益的。