报告题目：Time-Varying Factor Selection: A Sparse Fused GMM Approach
内容摘要：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. We estimate the conditional SDF for U.S. equity factors from 1972 to 2021. On the one hand, 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. On the other hand, the SFGMM strategy achieves the best risk-adjusted investment performance in the past four decades for out-of-sample performance comparison.
主讲人简介：Cui Liyuan is currently an associate professor in the Department of Economics and Finance at the City University of Hong Kong. She graduated with a bachelor's degree in mathematics and applied mathematics from Wuhan University in 2010, and received a Ph.D. in economics from Cornell University in 2017. Her main research areas include financial econometrics, high-dimensional data analysis, high-frequency trading, non-parametric statistics, capital asset pricing, etc. She has published papers in journals such as International Economic Review, Management Science, Journal of Econometrics, Journal of Environmental and Economic Management, Cities, 《经济研究》, and 《中国软科学》, and has presided over several National Natural Science Foundation and Hong Kong UGC Research Fund projects.
报告时间：2024年11月12日(周二)，16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：463 380 380
