发表机构
North-West University; National Institute for Theoretical and Computational Sciences (NITheCS)（西北大学; 理论与计算科学国家研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文对比信用卡违约损失率建模的流量三角形法与回归法，发现回归法能更好恢复U型分布，预测更准确，在IFRS 9框架下更优。
AI 中文摘要
在银行预测信贷损失时，使用流量三角形（ROTs）是估算违约损失率（LGD）风险参数的常见行业实践。我们利用信用卡数据，将这一行业实践与一种更复杂（尽管是经典）的基于回归的方法进行基准比较，该方法能够利用各种类型的输入变量来生成贷款级别的LGD估计。这种基于回归的方法能够明显恢复典型的“U型”经验LGD分布特征，而基于ROT的方法则无法做到。首先，我们批判性地审视了基于ROT的方法，并通过数据驱动的诊断识别出其多个缺点。然后，我们估计了一个两阶段的基于回归的LGD模型，并对每个组成部分（或“阶段”）的模型性能进行了有利评估。最后，我们按时间汇总每种方法产生的LGD估计，并将每个时间序列与随时间变化的平均经验损失率进行比较。基于ROT的汇总结果在大多数时间段内与经验率存在显著偏差，而基于回归的汇总结果则更紧密地跟随经验趋势。这些结果强调了相对于基于ROT的模型，基于回归的LGD模型具有更高的预测准确性。这意味着，在IFRS 9会计框架下估算LGD时，前者可能优于后者，因为该框架优先考虑准确性。
英文摘要
The use of run-off triangles (ROTs) is a common industry practice in estimating the loss given default (LGD) risk parameter when predicting credit losses in banking. We benchmark this industry practice using credit card data against a more sophisticated (though classical) regression-based approach, which is able to leverage various types of input variables in producing loan-level LGD-estimates. This regression-based approach can demonstrably recover the typical characteristics of the 'U-shaped' empirical LGD-distribution, which the ROT-based approach cannot do. First, we critically review the ROT-based approach and identify multiple demerits using data-driven diagnostics. We then estimate a two-stage regression-based LGD-model and favourably assess the model performance of each component (or 'stage'). Finally, we aggregate the LGD-estimates produced by each approach over time, and compare each time series to the mean empirical loss rate over time. The ROT-based aggregates diverge substantially from the empirical rate over most time periods, whilst the regression-based aggregates follow the empirical trends much closer. These results underscore the greater prediction accuracy of the regression-based LGD-model, relative to the ROT-based one. By implication, the former approach is probably better than the latter ROT-based approach when estimating the LGD under the IFRS 9 accounting framework, which prioritises accuracy.
Comments10146 words, 41 pages (inclusive of appendices), 19 Figures
