REGULARIZED GMM FOR TIME-VARYING MODELS WITH APPLICATIONS TO ASSET PRICING

• 成果类型: Article
• 署名作者: Cui, Liyuan; Feng, Guanhao; Hong, Yongmiao
• 署名单位: City University of Hong Kong; City University of Hong Kong; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12678
• 发表日期: 2024
• 页码: 851-883
• 关键词: generalized-method; conditioning information; sample properties; structural-change; series models; tests; heteroskedasticity; instability; estimators; selection

摘要

We propose a regularized generalized method of moments (RegGMM) approach to estimating time-varying coefficient models via a ridge fusion penalty with a high-dimensional set of moment conditions. RegGMM only requires a mild condition on the oscillations between consecutive parameter values, accommodating abrupt structural breaks and smooth changes throughout the sample period. RegGMM offers an alternative solution for estimating the time-varying stochastic discount factor model when pricing U.S. equity cross-sectional returns. Our time-varying estimate paths for factor risk prices capture changing performance across multiple risk factors and depict potential regime-switching scenarios. Finally, RegGMM demonstrates superior asset pricing and investment performance gains compared to alternative methods.