A0171
Title: Robust estimation of number of factors in high dimensional factor modeling via Spearman's rank correlation matrix
Authors: Zeng Li - Southern University of Science and Technology (China) [presenting]
Abstract: Determining the number of factors in high dimensional factor modelling is essential but challenging, especially when the data are heavy-tailed. A new estimator is introduced based on the spectral properties of Spearman's rank correlation matrix under the high dimensional setting, where both dimension and sample size tend to infinity proportionally. The estimator applies to scenarios where the common factors or idiosyncratic errors follow heavy-tailed distributions. It is proven that the proposed estimator is consistent under mild conditions. Numerical experiments also demonstrate the superiority of the estimator compared to existing methods, especially for the heavy-tailed case.
