Too Sharp to Be True? Illusory Gains in Regime-Weighted Conformal Prediction for Daily Rubber Price Changes

This study audits whether regime weighting can sharpen conformal forecast intervals without using target-period information or omitting the required finite-sample correction. Conformal prediction builds such intervals from past forecast errors. A natural refinement gives more weight to errors from days whose volatility resembles the forecast day. On daily natural-rubber prices, the refinement appears to work: intervals become about 20% narrower than plain split-conformal, with a significantly better Winkler score. This paper asks whether that gain is real. Three implementation choices are examined, one at a time. The first uses a regime signal that already sees the price move it is meant to predict. The second estimates the regime model on the same residuals the interval is calibrated on. The third omits a correction that the weighted quantile requires in finite samples. The forecast-feasible construction—predictive regime probabilities, a validation-fitted regime model, and the finite-sample correction—shows no detectable improvement over split-conformal, at a paired Winkler difference of +0.06 (95% CI −0.15 to +0.17). Applying the correction alone is not always enough: it removes the apparent advantage on the VMD-augmented ridge residuals, but the filtered comparison arm survives it on the AR(1) residuals at −0.54. Only withholding target-period information eliminates the artifact on both. Concentrated weights also leave some intervals unbounded, whereas adaptive conformal baselines remain finite throughout. A controlled simulation reproduces the same apparent gain where no regime information exists at all. Apparent sharpness must therefore be audited for information timing, calibration reuse, and the finite-sample correction.