Linear Approximations and Tests of Conditional Pricing Models

51 Pages Posted: 21 Sep 2006 Last revised: 11 Sep 2022

See all articles by Michael W. Brandt

Michael W. Brandt

Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)

David A. Chapman

McIntire School, University of Virginia

Multiple version iconThere are 2 versions of this paper

Date Written: September 2006

Abstract

We construct a simple reduced-form example of a conditional pricing model with modest intrinsic nonlinearity. The theoretical magnitude of the pricing errors (alphas) induced by the application of standard linear conditioning are derived as a direct consequence of an omitted variables bias. When the model is calibrated to either characteristics sorted or industry portfolios, we find that the alphas generated by approximation-induced specification error are economically large. A Monte Carlo analysis shows that finite-sample alphas are even larger. It also shows that the power to detect omitted nonlinear factors through tests based on estimated risk premiums can sometimes be quite low, even when the effect of misspecification on alphas is large.

Suggested Citation

Brandt, Michael W. and Chapman, David A., Linear Approximations and Tests of Conditional Pricing Models (September 2006). NBER Working Paper No. w12513, Available at SSRN: https://ssrn.com/abstract=930605

Michael W. Brandt (Contact Author)

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David A. Chapman

McIntire School, University of Virginia ( email )

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