Linear Approximations and Tests of Conditional Pricing Models

Michael W. Brandt and David Chapman ()

No 12513, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: G0 G10 G12 G14 (search for similar items in EconPapers)
Date: 2006-09
New Economics Papers: this item is included in nep-fin and nep-fmk
Note: AP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.nber.org/papers/w12513.pdf (application/pdf)

Related works:
Journal Article: Linear Approximations and Tests of Conditional Pricing Models* (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:12513

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w12513

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().