OLIVE: A Simple Method for Estimating Betas When Factors Are Measured with Error

44 Pages Posted: 2 Mar 2007 Last revised: 10 Nov 2011

See all articles by J. Ginger Meng

J. Ginger Meng

Stonehill College - Department of Business Administration

Gang Hu

Hong Kong Polytechnic University - School of Accounting and Finance

Jushan Bai

New York University (NYU) - Department of Economics

Date Written: March 30, 2010

Abstract

We propose a simple method for estimating betas (factor loadings) when factors are measured with error: Ordinary Least-squares Instrumental Variable Estimator (OLIVE). OLIVE is intuitive and easy to implement. OLIVE performs well when the number of instruments becomes large (can be larger than the sample size), while the performance of conventional instrumental variable methods and two-step GMM becomes poor or even infeasible. OLIVE is especially suitable for estimating asset return betas, since this is often a large N and small T setting. Intuitively, since all asset returns vary together with a common set of factors, one can use information contained in other asset returns to improve the beta estimate for a given asset. We apply OLIVE to modify the Fama-MacBeth method and reexamine the (C)CAPM. We find that in regressions where macroeconomic factors are included, using OLIVE instead of OLS beta estimates improves the R-squared significantly (e.g., from 31% to 80%). More importantly, our results based on OLIVE beta estimates help to resolve two puzzling findings in the prior literature: first, the sign of the average risk premium on the beta for the market return changes from negative to positive, consistent with the theory; second, the estimated value of average zero-beta rate is no longer too high (e.g., from 5.19% to 1.91% per quarter).

Keywords: factor model, beta estimation, measurement error, instrumental variable, many instruments, GMM

JEL Classification: G12, C30

Suggested Citation

Meng, J. Ginger and Hu, Gang and Bai, Jushan, OLIVE: A Simple Method for Estimating Betas When Factors Are Measured with Error (March 30, 2010). Journal of Financial Research, Vol. 34, 27-60, 2011. Available at SSRN: https://ssrn.com/abstract=966694

J. Ginger Meng (Contact Author)

Stonehill College - Department of Business Administration ( email )

320 Washington Street
Easton, MA 02357
United States
508-565-1986 (Phone)

HOME PAGE: http://www.stonehill.edu/directory/j-ginger-meng/

Gang Hu

Hong Kong Polytechnic University - School of Accounting and Finance ( email )

M1038, Li Ka Shing Tower
Hung Hom, Kowloon
Hong Kong
(852) 3400 8455 (Phone)

HOME PAGE: http://ganghu.org

Jushan Bai

New York University (NYU) - Department of Economics ( email )

269 Mercer Street, 7th Floor
New York, NY 10003
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
351
Abstract Views
1,466
rank
84,760
PlumX Metrics