Stabilising Lifetime PD Models under Forecast Uncertainty

Credit risk
IFRS 9
CECL
State-space models
Kalman filtering
Transition matrices
PD term structure
Uncertainty & robustness
Published

September 7, 2025

Keywords

lifetime PD, point-in-time PD, macroeconomic scenarios, forecast error propagation, anchored observation model, stochastic stability, convergence in probability, procyclicality, Monte Carlo simulation, transition-matrix projection

Code arXiv

Abstract

Estimating lifetime probabilities of default (PDs) under IFRS9 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 long–run economic state into the filter. The resulting error dynamics exhibit asymptotic stochastic stability, ensuring convergence in probability of the lifetime PD term structure. Simulation on a synthetic corporate portfolio confirms that anchoring reduces forecast noise and delivers smoother, more interpretable projections.

Anchored state-space approach for stable lifetime PD; less drift, less procyclicality. Code and full reproducibility.

Materials

How to cite

Rostampour, V. (2025). Stabilising Lifetime PD Models under Forecast Uncertainty. arXiv:XXXXXXXX.

@misc{rostampour2025akf,
  title        = {Stabilising Lifetime PD Models under Forecast Uncertainty},
  author       = {Rostampour, Vahab},
  year         = {2025},
  eprint       = {http://arxiv.org/abs/2509.10586},
  archivePrefix= {arXiv},
  primaryClass = {q-fin.RM}
}