BAFI Academic Seminarhttps://weatherhead.case.edu/events/2016/04/15/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.
Dept. Admin., BAFI Dept
||Friday, April 15, 2016 from 10:30 a.m. to noon
||Peter B. Lewis Building, Room 03
11119 Bellflower Road
Cleveland, OH 44106-7235
||Speaker(s): Prof. Joonki Noh
||Joonki Noh paper
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