Research
Publications
“Tensor PCA for Factor Models” with Andrii Babii and Eric Ghysels, R&R
Abstract: Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing such dependencies. A tensor factor model describes the multidimensional panel as a sum of a low-rank component and idiosyncratic noise, generalizing traditional factor models for two-dimensional panels. We propose an estimation algorithm called tensor principal component analysis (TPCA) to estimate factors and loadings. We provide the asymptotic distribution theory and propose a test for the number of factors in a tensor factor model. The asymptotic results are supported by the Monte Carlo experiments, and the new tools are applied to sorted portfolios.
Recommended citation: Babii, Andrii, Ghysels, Eric, and Pan, Junsu. “Tensor PCA for Factor Models.” (2024).
Working Papers
“Missing Financial Data: Filling the Tensor Blanks” with Andrii Babii, Eric Ghysels, and Jiaxi Li
“Dynamic Portfolio Selection with Regularization”
Work in Progress
“Conditional Asset Pricing Factor Models with Firm Characteristics Tensor Data” with Andrii Babii and Eric Ghysels
“Identification and Estimation of Factor Models Through Coskewness Tensor” with Andrii Babii and Eric Ghysels