Expected credit loss impairment: early recognition vs. income volatility

一项新的会计原则，即所谓的国际财务报告准则第9号，要求银行 ** 以前瞻性的方式确认预期信贷损失。实施IFRS 9的一种方式是基于 ** 在管理监管资本的背景下使用的杠杆模型。这种方式特别适用于CWB** 金融集团，该集团目前正在转变其管理监管资本的方法。然而，《国际财务报告准则》第9号 ** 要求根据贷款的信用风险自初始确认以来是否显著增加 **，对贷款进行不同的处理。因此，IFRS 9的实施需要定义和衡量什么 ** 构成信用风险的显著增加。本拟议项目的主题是找到一种适当的统计方法来确定信贷风险 ** 显著增加的贷款。虽然应及早认识到 ** 信用风险，但过于宽泛地界定大幅增加会导致收入波动和对信用 ** 风险的过高估计。利用应用统计学和时间序列分析的方法，本项目的目标是找到一种适当的分类方法，将早期识别信用风险和收入波动性之间的权衡考虑在内。

A new accounting principle, the so-called International Financial Reporting Standard (IFRS) 9, requires banks**to recognize expected credit losses in a forward-looking way. One way of implementing IFRS 9 is based on**leveraging models used in the context of managing regulatory capital. This way applies particularly to CWB**Financial Group, which is currently transitioning its approach of managing regulatory capital. However, IFRS 9**asks for different treatments of loans depending on whether or not their credit risk has significantly increased**since initial recognition. Therefore, the implementation of IFRS 9 needs a definition and measuring of what**constitutes a significant increase in credit risk. Finding a suitable statistical way to identify loans with**significantly increased credit risk is the topic of this proposed project. While credit risk should be recognized**early, defining a significant increase too widely leads to income volatility and too high estimations of credit**risk. Using methods from applied statistics and time series analysis, the goal of this project is to find a suitable**classification that takes the trade-off between early recognition of credit risk and income volatility into**consideration.