SEMIPARAMETRIC ESTIMATION OF RISK-RETURN RELATIONSHIPS

Juan Carlos Escanciano, Juan Carlos Pardo-Fernández and Ingrid Van Keilegom ()
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Juan Carlos Pardo-Fernández: Universidade de Vigo

No 2013-004, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

Abstract: This article proposes semiparametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator is that it does not require a parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is non-standard due to the presence of estimated factors. We provide simple sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the finite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using nonparametric estimates, we find a positive and significant price of risk in our semiparametric setting.

Pages: 30 pages
Date: 2013-09
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Related works:
Journal Article: Semiparametric Estimation of Risk–Return Relationships (2017)
Working Paper: Semiparametric Estimation of Risk-return Relationships (2017)
Working Paper: Semiparametric Estimation of Risk-return Relationships (2013)
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