Using Stocks or Portfolios in Tests of Factor Models

Abstract

The speaker examines the asymptotic efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows factor loadings, and consequently risk premia, to be estimated more precisely. The speaker shows analytically and demonstrates empirically that the more efficient estimates of betas from creating portfolios do not lead to lower asymptotic variances of factor risk premia estimates. Instead, the standard errors of factor risk premia estimates are determined by the cross-sectional distribution of factor loadings and residual risk. Creating portfolios destroys information by shrinking the dispersion of betas and leads to higher asymptotic standard errors of risk premia estimates.

About the speaker

Prof. Liu Jun received his PhD in Physics from the University of Texas at Austin in 1988, and PhD in Finance from the Graduate School of Business at Stanford University in 2002. He was Assistant Professor at the University of California at Los Angeles from 1999 to 2005. He joined the University of California at San Diego in 2005, and is currently Associate Professor of Finance at the Rady School of Management.

Prof. Liu’s research interests include theoretical and empirical asset pricing and macroeconomics. His current research involves optimal convergence trades, asset price tests, and new Keynesian models. His work has been published in the Journal of Finance, the Journal of Financial Economics, the Review of Financial Studies, the Journal of Economic Theory, and the Accounting Review.

Prof. Liu won a Michael Brennan Award for the best paper by the Review of Financial Studies in 2005. He recently became a recipient of the “Thousand Talents” Program, one of the most prestigious awards granted by the Chinese central government.