Regularized GMM for Time-Varying Models with Application to Asset Pricing

43 Pages Posted: 8 Apr 2021 Last revised: 18 Jul 2022

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

Date Written: July 18, 2022

Abstract

We develop a novel method to estimate time-varying GMM models via a ridge fusion regularization
scheme, which allows for a high dimension of instrumental variables. Our method
relaxes restrictions on the types of time variation (abrupt or smooth) and their sources and can
be implemented by a one-step procedure. Under regularizations, we have established consistency
and derived the limiting distribution for independent and dependent observations. This
regularizedGMMmethod provides an alternative solution for estimating the dynamic stochastic
discount factor (SDF) model by utilizing a large cross section and many conditioning variables.
The simulation study shows its robust performance for various data generating processes and
sample sizes. We apply our method to U.S. equities from 1972 to 2021. Our time-varying estimates
for factor risk price (SDF loadings) respond to changes in performance for multiple risk
factors and summarize potential regime-switching scenarios. By outperforming multiple benchmark
models, we demonstrate the gains in asset pricing and investment performance for our
regularized GMM model for in-sample and out-of-sample analysis.

Keywords: GMM, regularization, ridge fusion penalty, stochastic discount factor, time-varying model.

JEL Classification: C13, C14, C55, C58, C61, G12

Suggested Citation

Cui, Liyuan and Feng, Guanhao and Hong, Yongmiao, Regularized GMM for Time-Varying Models with Application to Asset Pricing (July 18, 2022). Available at SSRN: https://ssrn.com/abstract=3814520 or http://dx.doi.org/10.2139/ssrn.3814520

Liyuan Cui (Contact Author)

City University of Hong Kong ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Guanhao Feng

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon Tong
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)

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