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Semi Parametric Estimation Of Risk-Return Relationships

  • Juan Carlos Escanciano

    ()

    (Indiana University)

  • Juan Carlos Pardo-Fernández

    ()

    (University of Vigo)

  • Ingrid Van Keilegom

    ()

    (Université catholique de Louvain)

This article proposes semi-parametric 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 nite 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 non-parametric estimates, we find a positive and significant price of risk in our semi-parametric setting.

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File URL: http://www.iub.edu/~caepr/RePEc/PDF/2013/CAEPR2013-004.pdf
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Paper provided by Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington in its series Caepr Working Papers with number 2013-004.

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Length: 30 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:inu:caeprp:2013004
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