【题  目】Time-Varying Factor Selection: A Sparse Fused GMM Approach

【时  间】2024年6月18日（星期二），14:00-15:30

【地  点】后主楼1722会议室

【主讲人】崔丽媛 助理教授（香港城市大学经济及金融系）

【主持人】李亚男 副教授  （北京师范大学经济与工商管理学院）

摘要：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.

简介： 崔丽媛，香港城市大学经济及金融系助理教授。她在武汉大学获应用数学和金融学双学士学位；在美国康奈尔大学（Cornell University）获经济学博士学位。她的主要研究方向是金融计量经济学、资产定价等。她曾以第一作者在《Management Science》、《International Economic Review》、《Journal of Econometrics》、《Journal of Environmental Economics and Management》、《经济研究》等上发表论文。曾主持过5项香港研究资助局（GRF）基金和1项国家自然科学基金青年项目。