Stabilising Lifetime PD Models under Forecast Uncertainty

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 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.