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Linear Approximations and Tests of Conditional Pricing Models

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Author Info
Michael W. Brandt
David A. Chapman

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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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 12513.

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Date of creation: Sep 2006
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Handle: RePEc:nbr:nberwo:12513

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Find related papers by JEL classification:
G0 - Financial Economics - - General
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies

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