Linear Approximations and Tests of Conditional Pricing Models

57 Pages Posted: 9 Mar 2006 Last revised: 22 Jun 2017

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: June 19, 2017

Abstract

If a nonlinear risk premium in a conditional asset pricing model is approximated with a linear function, as is commonly done in empirical research, the fitted model is misspecified. We use a generic reduced-form model economy with moderate risk premium nonlinearity to examine the size of the resulting misspecification-induced pricing errors. Pricing errors from moderate nonlinearity can be large, and a version of a test for nonlinearity based on risk premiums rather than pricing errors has reasonable power properties after properly controlling for the size of the test. We conclude by examining the importance of moderate nonlinearity in the context of the investment-specific technology shock models of Papanikolaou (2011) and Kogan and Papanikolaou (2014).

JEL Classification: G12, C13, C22

Suggested Citation

Brandt, Michael W. and Chapman, David A., Linear Approximations and Tests of Conditional Pricing Models (June 19, 2017). Available at SSRN: https://ssrn.com/abstract=889376 or http://dx.doi.org/10.2139/ssrn.889376

Michael W. Brandt

Duke University - Fuqua School of Business ( email )

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National Bureau of Economic Research (NBER)

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David A. Chapman (Contact Author)

McIntire School, University of Virginia ( email )

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