The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
伪因子困境:重尾椭圆因子模型中的稳健推断
Standard methods for determining the number of factors often overestimate the true number when data exhibit heavy-tailed randomness, misinterpreting noise-induced outliers as genuine factors. This paper addresses this challenge within the framework of Elliptical Factor Models (EFM), which accommodate both heavy tails and potential non-linear dependencies common in real-world data. We demonstrate, both theoretically and empirically, that heavy-tailed noise generates spurious…