Stabilising Lifetime PD Models under Forecast Uncertainty
Vahab Rostampour
根据IFRS 9和CECL估计默认(PD)的生命周期概率,需要预测多年的实时转换矩阵。 一个持续的弱点是宏观经济预测错误在视野中加剧,产生不稳定和不稳定的PD术语结构。 本文重新划分了状态-空间框架中的问题,并表明直接的卡尔曼过滤器留下了非消失的可变性。 然后,我们引入了一个锚定观测模型,该模型将中性的长期经济状态融入过滤器中。 由此产生的误差动力学表现出渐近的随机稳定性,确保了终身PD项结构概率的收敛。 合成企业组合的模拟证实,锚定可降低预测噪声,并提供更平滑、更可解释的预测。
Estimating lifetime probabilities of default (PDs) under IFRS 9 and CECL requires projecting point–in–time transition matrices over multiple years. A persistent weakness is that macroeconomic forecast errors compound across horizons, producing unstable and volatile PD term structures. This paper reformulates the problem in a state–space framework and shows that a direct Kalman filter leaves non–vanishing variability. We then introduce an anchored observation model, which incorporates a neutral l...