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Activity Number: 49 - Statistical Inference for Large-Scale Financial Data
Type: Invited
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #321864
Title: Inference on Risk Premia Without a Fully Specified Factor Model
Author(s): Dacheng Xiu* and Stefano Giglio
Companies: University of Chicago and University of Chicago
Keywords: Three-Pass Estimator ; Empirical Asset Pricing Models ; PCA ; Latent Factors ; Omitted Factors

We propose a new method to estimate the risk premium of observable factors in a linear asset pricing model, which is valid even when the observed factors are just a subset of the full set of factors that drive asset prices. Standard methods to estimate risk premia are biased in the presence of omitted priced factors that are correlated with the observed factors. We show that unbiased estimates of risk premia for observed factors can be obtained by performing two-pass cross-sectional estimation on any rotation of the true factor space, as long as it includes the observable factors and spans the entire factor space. Motivated by this rotation invariance result, our approach uses principal components to recover the factor space, and combines the estimated principal components with each observed factor to obtain a consistent estimate of its risk premium. The methodology also accounts for potential measurement error in the observed factors, and detects when such factors are spurious or even useless. The methodology exploits the blessings of dimensionality, and we apply it to a large panel of equity portfolios to estimate risk premia for several workhorse

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