When 3% means nothing: Calibrating escalation limits to a bank’s own forecasting error distribution

Forecasting accuracy for Net Interest Income (NII) and Interest Rate Risk in the Banking Book (IRRBB) is central to banks’ earnings stability, balance-sheet management, and supervisory credibility. Yet many institutions continue to apply fixed deviation thresholds (for example, 3/4/5%) to govern forecast performance, even though forecast uncertainty widens with the horizon and may exhibit heavy-tailed behavior. Such limits therefore lack a consistent probabilistic interpretation and often misalign with the statistical properties of the underlying forecasting process. This paper develops an integrated, probability-coherent framework for monitoring NII forecasterrors and assessing IRRBB limit breaches. First, drawing on the Federal Reserve’s use of Root Mean Squared Error (RMSE) and fan charts to communicate forecast uncertainty, we construct horizon-specific, quantile-anchored thresholds that preserve consistent meaning across forecast horizons. The framework incorporates interval-forecast evaluation (unconditional and conditional coverage tests), quantile elicitability, bias-dispersion decomposition, and extreme-value modeling of rare outcomes. Second, we extend the methodology to IRRBB by quantifying the probability that limits on changes in NII (?NII) are breached solely due to forecast or model uncertainty.