Online Forecast Aggregation with Quantile-Anchored Tail Protection

A source forecast can improve expected continuous ranked probability score (CRPS) while substantially distorting the probability assigned to extreme events. We propose a quantile-anchored source transfer method (QAST), which lets source models reshape a baseline forecast only within cells defined by its tail quantiles, while preserving the baseline's exceedance probability and protected quantiles. Any convex combination of these forecasts keeps the combined-ratio supremum distance (CRSD) at a fixed threshold within a user-chosen radius of the baseline's CRSD, for every outcome sequence and every choice of weights. Within the protected class, we establish sublinear and constant regret for the aggregate forecast's finite-node cell-conditional square score against fixed transferred experts. An explicit iid construction isolates within-cell tail-shape misspecification and shows improvement in expected CRPS and limiting CRSD. On EUPPBench, QAST reduces annual proper-score losses relative to unprotected mixtures using the same weights under two baselines.