Speaker: Dr. 陈齐辉 (Qihui Chen)

Title: Semiparametric Conditional Factor Models: Estimation and Inference

Schedule: March 27, Wed 1:30-3:00 PM

Location: 诚明楼（Chengming Hall） RM315

Introduction: 陈齐辉，香港中文大学（深圳）经管学院经济学副教授，他于2017年从加利福尼亚大学圣地亚哥分校获得经济学博士学位。他主要研究领域为计量经济学、机器学习等，并在Journal of Econometrics、Quantitative Economics、Econometric Theory 等国际核心期刊发表多篇学术论文。陈齐辉的研究获得国家自然科学基金青年科学基金项目和国家自然科学基金面上项目的支持。

Abstract: This paper introduces a simple and tractable sieve estimation of semiparametric conditional factor models with latent factors. We establish large-N-asymptotic properties of the estimators without requiring large T. We also develop a simple bootstrap procedure for conducting inference about the conditional pricing errors as well as the shapes of the factor loading functions. These results enable us to estimate conditional factor structure of a large set of individual assets by utilizing arbitrary nonlinear functions of a number of characteristics without the need to pre-specify the factors, while allowing us to disentangle the characteristics' role in capturing factor betas from alphas (i.e., undiversifiable risk from mispricing). We apply these methods to the cross-section of individual U.S. stock returns and find strong evidence of large nonzero pricing errors that combine to produce arbitrage portfolios with Sharpe ratios above 3. We also document a significant decline in apparent mispricing over time.