Semi-Parametric Estimation of Factor Risk-Premia

43 Pages Posted: 30 Jan 2018 Last revised: 28 Feb 2019

See all articles by Maziar Kazemi

Maziar Kazemi

Arizona State University (ASU) - Finance Department

Date Written: February 26, 2019

Abstract

This paper shows that factor risk premia can be consistently estimated using a semi-parametric estimate of the stochastic discount factor without requiring a correctly specified linear factor model. We use a minimum discrepancy objective function to construct a stochastic discount factor from asset returns using only the economic assumption of no arbitrage. The stochastic discount factor and factor risk-premia are estimated using only data on portfolio returns and factor realizations: The same data used when evaluating linear models. The econometrics are applications of standard extremum estimator arguments and the Delta Method, making inference simple. In simulations, the estimated risk-premia have low root mean squared errors and are comparable to classic two-pass estimates even when the model is correctly specified. Empirical estimates of popular traded factors are close to their mean excess returns. For non-traded factors, we find that intermediary leverage and consumption growth carry risk-premia, while employment growth does not. A final application shows that the estimated risk-premia can be used as an extra moment condition to discipline the creation of factor mimicking portfolios.

Keywords: Entropy, Factor Models, Arbitrage Pricing Theory, Risk Premia, Misspecification, Minimum Discrepancy

JEL Classification: G12, G14, C13, C14

Suggested Citation

Kazemi, Maziar, Semi-Parametric Estimation of Factor Risk-Premia (February 26, 2019). Available at SSRN: https://ssrn.com/abstract=3103295 or http://dx.doi.org/10.2139/ssrn.3103295

Maziar Kazemi (Contact Author)

Arizona State University (ASU) - Finance Department ( email )

W. P. Carey School of Business
PO Box 873906
Tempe, AZ 85287-3906
United States

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