Working Paper: CEPR ID: DP12085
Authors: Elena Manresa; Francisco Pearanda; Enrique Sentana
Abstract: Asset pricing models with potentially too many risk factors are increasingly common in empirical work. Unfortunately, they can yield misleading statistical inferences. Unlike other studies focusing on the properties of standard estimators and tests, we estimate the sets of SDFs and risk prices compatible with the asset pricing restrictions of a given model. We also propose tests to detect problematic situations with economically meaningless SDFs uncorrelated to the test assets. We confirm the empirical relevance of our proposed estimators and tests with Yogo's (2006) linearized version of the consumption CAPM, and provide Monte Carlo evidence on their reliability in finite samples.
Keywords: continuously updated GMM; factor pricing models; set estimation; stochastic discount factor; underidentification tests
JEL Codes: G12; C52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| overspecified asset pricing models (G19) | misleading statistical inferences (C20) |
| uncorrelated risk factors (C10) | overspecification (C60) |
| overspecification (C60) | underidentification of model parameters (C51) |
| proposed tests (C12) | diagnose economically meaningless situations (D00) |
| admissible SDFs in linearized version of Yogo's model (C69) | lack of identification (Y70) |
| null hypothesis that all admissible SDFs have zero means (C12) | complete overspecification (C60) |
| simulations (C15) | findings are not due to lack of power in tests (C52) |
| rank deficiencies in matrix of covariances for selected factors (C38) | econometric challenges posed by overspecified models (C50) |