Semiparametric Estimation of Risk-return Relationships
(2017) Journal of Business and Economic Statistics — Vol. 35, n° 1, p. 40-52 (2017)
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- 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 sucient 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 nonparametric estimates, we nd a positive and signicant price of risk in our semiparametric setting.
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Citations
Escanciano, J. C., Pardo-Fernández, J. C., & Van Keilegom, I. (2017). Semiparametric Estimation of Risk-return Relationships. Journal of Business and Economic Statistics, 35(1), 40-52. https://doi.org/10.1080/07350015.2015.1052879 (Original work published 2017)