Purpose

The purpose of this paper is to present a comprehensive framework for assisting lending banks in their current expected credit losses (CECL) forthcoming computations.

Design/methodology/approach

The bottom-up approach requires multiple steps including the spline method for identifying optimal segments in the lifetimes of loans, Poisson regressions for evaluating the explanatory variables and hazard rate probes for gaining inferences toward the expected credit losses and their projected schedule.

Findings

The CECL paradigm has both advantages and disadvantages, as discussed hereafter.

Practical implications

The model is practical, accurate in the sense that provisions are properly and timely allocated, it can be programmed and it relies on merely a few mild assumptions, thus it can be conveniently calibrated to fit broad macroeconomic scenarios.

Originality/value

This study provides background on the subject, motivate each module, construct the advised model, assemble a pseudo-database, demonstrate the functionality of the procedures and further draw conclusions on the effectiveness of the current strategy.

中文翻译：

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当前预期信用损失的样条风险模型

目的

本文的目的是提出一个全面的框架，以帮助贷款银行进行当前预期信用损失 (CECL) 即将进行的计算。

设计/方法/方法

自下而上的方法需要多个步骤，包括用于识别贷款生命周期中最佳部分的样条方法、用于评估解释变量的泊松回归和用于推断预期信用损失及其预计时间表的风险率探测。

发现

CECL 范式既有优点也有缺点，如下所述。

实际影响

该模型是实用的、准确的，因为拨备得到适当和及时的分配，它可以编程并且仅依赖于一些温和的假设，因此可以方便地进行校准以适应广泛的宏观经济情景。

原创性/价值

这项研究提供了该主题的背景，激发了每个模块，构建了建议的模型，组装了一个伪数据库，展示了程序的功能，并进一步得出了关于当前策略有效性的结论。