Aggregate Calibration Can Mask Stress-State Undercoverage: Bounded-Memory Forecast Intervals under Recurrent Financial Stress
Ruihang Han, Li-Hsiang Lin
Abstract
Sequential forecast intervals are often evaluated by aggregate coverage even when calibration memory is bounded. This can be misleading when economically important states recur after long absences: a recent-only buffer may satisfy its overall coverage target while no longer containing errors from earlier episodes of the returning state. We study this problem in next-day financial range forecasting, holding the forecasting model, conformity score, adaptive update, and memory budget constant while varying the historical residual information available at each query. Recent-only first-in, first-out (FIFO) calibration is near nominal in aggregate but undercovers substantially during high-volatility states in both an exchange-traded fund development panel and a fixed stock panel. An unbounded state-history reference yields a lower observed stress-period interval score; Rare replay yields lower high-VIX interval scores than the matched Recent-n and Hybrid-n controls in both panels, and the state-structured versus recent-only contrast is largest near stress re-entry. Bounded calibration memory is therefore part of the forecasting information design, and aggregate calibration alone can conceal state-specific reliability failures.
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