Time-Varying Factor Selection: A Sparse Fused GMM Approach

43 Pages Posted: 1 May 2023 Last revised: 22 Feb 2024

See all articles by Liyuan Cui

Liyuan Cui

City University of Hong Kong

Guanhao Feng

City University of Hong Kong (CityU)

Yongmiao Hong

Cornell University - Department of Economics

Jiangshan Yang

City University of Hong Kong (CityU) - Department of Economics & Finance

Date Written: January 31, 2024

Abstract

This paper proposes a new approach for estimating a time-varying coefficient model under the GMM framework. Our sparse fused GMM (SFGMM) method provides simultaneous specification and estimation for time-varying parameters, heterogeneous structural breaks, and time-varying sparsity of a potentially high dimension of covariates. We derive large sample properties for our estimator with and without prior knowledge of structural changes and test the conditional stochastic discount factor (SDF) model. Our method addresses the "factor zoo" challenge by providing a new perspective for time-varying factor selection.
First, our asymptotic theory on the time-varying specified model suggests rejecting the fixed model hypothesis, indicating the significant factors and their identities change over time. Second, we find the collective explanatory power of risk factors is high during periods of high interest rates or high inflation but declines when market liquidity is low. Third, the SFGMM strategy achieves the best risk-adjusted investment performance in the past four decades for out-of-sample performance comparison. Finally, we evaluate the unsynchronized factor discovery to accommodate real-time academic publication timings and find many factors are no longer selected or significant after publication.

Keywords: factor model, fused Lasso, structural breaks, time-varying coefficient model, variable selection

JEL Classification: C14, G11, G12

Suggested Citation

Cui, Liyuan and Feng, Guanhao and Hong, Yongmiao and Yang, Jiangshan, Time-Varying Factor Selection: A Sparse Fused GMM Approach (January 31, 2024). Available at SSRN: https://ssrn.com/abstract=4431543 or http://dx.doi.org/10.2139/ssrn.4431543

Liyuan Cui

City University of Hong Kong ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Guanhao Feng (Contact Author)

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Hong Kong

Yongmiao Hong

Cornell University - Department of Economics ( email )

Department of Statistical Science
414 Uris Hall
Ithaca, NY 14853-7601
United States
607-255-5130 (Phone)
607-255-2818 (Fax)

Jiangshan Yang

City University of Hong Kong (CityU) - Department of Economics & Finance ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

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