报告人： Dacheng Xiu, Associate Professor of Econometrics and Statistics and IBM Corporation Faculty Scholar, The University of Chicago Booth School of Business
时间：2017-04-26 14:00 ~ 15:00
地点：K01, Guanghua Hotel
The asset pricing literature has produced hundreds of potential risk factors. Organizing this “zoo of factors” and distinguishing between useful, useless, and redundant factors require econometric techniques that can deal with the curse of dimensionality. We propose a model-selection method that allows us to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what is explained by a high-dimensional set of existing factors. Our procedure selects the best parsimonious model out of the large set of existing factors, and uses it as the control in making statistical inference about the contribution of new factors. Our inference allows for model selection mistakes, and is therefore more reliable in finite sample. We derive the asymptotic properties of our test and apply it to a large set of factors proposed in the literature. We show that despite the fact that hundreds of factors have been proposed in the last 30 years, some recent factors – like profitability – have statistically significant explanatory power in addition to existing ones. We confirm the effectiveness of our procedure to discriminate factors in a recursive and out-of-sample experiment, and show that it results in a parsimonious model with a small number of factors and high cross-sectional explanatory power, even as the pool of candidate factors has expanded dramatically.
About the Speaker:
Dacheng Xiu is Associate Professor of Econometrics and Statistics and IBM Corporation Faculty Scholar at The University of Chicago Booth School of Business. He is interested in developing and applying statistical methodologies to exploit the economic implication of financial data. His prior research involves risk measurement and management with high-frequency data and econometric modeling of derivatives. His current work focuses on developing machine learning solutions to big-data problems in empirical asset pricing. His work has appeared in the Econometrica, Journal of Econometrics, Journal of the American Statistical Association, and he has been invited to publish in the Journal of Business and Economic Statistics. Xiu has presented his work at various conferences and university seminars. He is an Associate Editor for the Journal of Econometrics, and also serves as a referee for many journals in econometrics, statistics, and finance. Xiu earned his PhD and MA in applied mathematics from Princeton University, where he studied at the Bendheim Center for Finance. Before that, he obtained a BS in mathematics from the University of Science and Technology of China in Hefei, China. Additionally, Xiu’s professional experience includes work with TYKHE Capital LLC in New York and Citigroup in their capital markets and banking division.