Amortized Neural Filtering for Calibrated Posteriors in Statistical Arbitrage
Statistical arbitrage uses estimates of residual spread states but ignores uncertainty. The Neural Filter Estimator (NFE) is trained on simulated mean-reverting processes to return a calibrated posterior distribution for each spread. Results show this posterior accurately reflects its own uncertainty, significantly reducing forecast errors on Korean equities compared to conventional point estimates.