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计量\金融与大数据分析workshop:Time-Varying Factor Selection: A Sparse Fused GMM Approach
发布日期：2023-06-16 12:00 来源：
时变因子选择：稀疏融合GMM方法
时间：2023年6月16日（周五）10:00 AM -- 11:30 AM
地点（线上）：腾讯会议参会号码：666-633-366
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
主讲人：崔丽媛, 香港城市大学经济与金融系助理教授
摘要：
This paper proposes a sparse fused GMM approach (SFGMM) to estimate a sparse time-varying coefficient model for selecting factors with heterogeneous structural breaks. SFGMM offers an alternative estimation to the dynamic stochastic discount factor model, where factor risk prices are sparse and time-varying, employing a highdimensional set of conditioning variables and test assets. Evaluating U.S. equity factors, we find ours outperforms several benchmark models, improving asset pricing and investment performance and providing insights into time-varying factor selection. Our results indicate risk factors have the strongest explanatory power when the aggregate dividend yield or default yield is high, but their effectiveness is reduced when market liquidity is low. Moreover, our study reveals the selection of factors changes over time, with some previously successful factors, such as momentum and idiosyncratic volatility, disappearing in the recent decade, while new factors, such as betting-against-beta and expected growth, have emerged.
主讲人简介：
崔丽媛，现为香港城市大学经济与金融系助理教授。2010年本科毕业于武汉大学数学与应用数学专业，2017年获得美国康奈尔大学经济学博士学位。主要研究方向包括金融计量经济学，高维数据分析，高频交易，非参数统计，资本资产定价等。已在Management Science, Journal of Econometrics, Cities，《经济研究》，和《中国软科学》等期刊发表论文。
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计量\金融与大数据分析workshop:Time-Varying Factor Selection: A Sparse Fused GMM Approach
发布日期：2023-06-16 12:00 来源：
时变因子选择：稀疏融合GMM方法
时间：2023年6月16日（周五）10:00 AM -- 11:30 AM
地点（线上）：腾讯会议参会号码：666-633-366
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
主讲人：崔丽媛, 香港城市大学经济与金融系助理教授
摘要：
This paper proposes a sparse fused GMM approach (SFGMM) to estimate a sparse time-varying coefficient model for selecting factors with heterogeneous structural breaks. SFGMM offers an alternative estimation to the dynamic stochastic discount factor model, where factor risk prices are sparse and time-varying, employing a highdimensional set of conditioning variables and test assets. Evaluating U.S. equity factors, we find ours outperforms several benchmark models, improving asset pricing and investment performance and providing insights into time-varying factor selection. Our results indicate risk factors have the strongest explanatory power when the aggregate dividend yield or default yield is high, but their effectiveness is reduced when market liquidity is low. Moreover, our study reveals the selection of factors changes over time, with some previously successful factors, such as momentum and idiosyncratic volatility, disappearing in the recent decade, while new factors, such as betting-against-beta and expected growth, have emerged.
主讲人简介：
崔丽媛，现为香港城市大学经济与金融系助理教授。2010年本科毕业于武汉大学数学与应用数学专业，2017年获得美国康奈尔大学经济学博士学位。主要研究方向包括金融计量经济学，高维数据分析，高频交易，非参数统计，资本资产定价等。已在Management Science, Journal of Econometrics, Cities，《经济研究》，和《中国软科学》等期刊发表论文。
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