A1124
Title: Determining the Number of Common Functional Factors with Twice Cross-Validation
Authors: Hui Jiang - Huazhong University of Science and Technology (China) [presenting]
Lei Huang - South West Jiaotong University (China)
Abstract: The semiparametric factor model serves as a vital tool to describe the dependencepatterns in the data. It recognizes that the common features observed in the data areactually explained by functions of specific exogenous variables. Unlike traditional factor models, where the focus is on selecting the number of factors, our objective here isto identify the appropriate number of common functions, a crucial parameter in thismodel. In this paper, we develop a novel data-driven method to determine the number of functional factors using cross validation (CV). Our proposed method employsa two-step CV process that ensures the orthogonality of functional factors, which werefer to as Functional Twice Cross-Validation (FTCV). Extensive simulations demonstrate that FTCV accurately selects the number of common functions and outperformsexisting methods in most cases. Furthermore, by specifying market volatility as theexogenous force, we provide real data examples that illustrate the interpretability ofselected common functional factors.
