BAFI Academic Seminar

To attenuate an inherent errors-in-variables bias, portfolios are widely employed for risk premium estimation; but portfolios might diversify away and thus mask relevant risk- or return-related features of individual assets. We propose a resolution that allows the use of individual assets while avoiding the bias. It hinges on specific instrumental variables, factor sensitivities (β’s) calculated from alternate observations. Closed-form asymptotics are provided for large cross-sections and time-series. Simulations indicate that the IV method delivers unbiased risk premium estimates and well-specified tests with adequate power in small samples. Empirical implementation finds some evidence of significant risk premiums for the size and book-to-market. However, when controlling for non-β characteristics, estimated risk premiums are insignificant for the CAPM, size, book-to-market, investments, profitability, and the liquidity-adjusted CAPM.  This paper is co-authored with Narasimhan Jegadeesh, Emory University; Kuntara Pukthuanthong, University of Missouri; Richard Roll, Caltech; and Junbo wang, Louisiana State University.

 Fee: [Yes/No/Varies]

Contact Information:
Tedda Nathan
Dept. Admin., BAFI Dept
txn2@case.edu
216-368-2040 (T)
216-368-6249 (F)