非极端回收不良贷款的回收率预测研究

Forecasting Models for Non-performing Loans of Non-extreme Recovery

本文依托Loss Metric数据库,对非极端回收不良贷款的回收率预测模型进行了研究。文章通过对非极端回收不良贷款的回收率进行Beta-正态变换和Logit变换,结合影响不良贷款回收率的众多因素,进行了从简单到复杂、从单笔贷款债务人到多笔贷款债务人的逐级建模,并剖析了影响回收率的各个因素。实证结果表明,单笔贷款债务人的回收率模型可作为多笔贷款债务人回收率预测的基准;从单笔贷款债务人模型中可以更直观的分析各个不同的变量对回收率的影响。

Using the data from Lossmetric, this paper tries to build forecasting models for non-performing loans of non-extreme recovery. The paper first uses beta-normal transformation and logit transformation to process recovery rates, and gives an individual analysis for each impacting factor on recovery rate. Then the paper proceeds a model-establishing process from simple model to complex model, from single debt obligor＇s model to multi-debt obligor＇s model, aiming at a comprehensive investigation on the impacting factors. Empirical results show that both the single debt obligor＇s model and the multi-debt obligor＇s model have good predicting power, moreover the single debt obligor＇s model itself can reflect the relationship between the recovery rate and the impacting factors, and it can be used as the foundation of multi-debt obligor＇ s model.