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Inference for variance risk premium

Inference for variance risk premium With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.Design/methodology/approachThe authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.FindingsThis study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.Research limitations/implicationsThis study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.Originality/valueThis study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

Inference for variance risk premium

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2044-1398
DOI
10.1108/cfri-04-2020-0044
Publisher site
See Article on Publisher Site

Abstract

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.Design/methodology/approachThe authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.FindingsThis study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.Research limitations/implicationsThis study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.Originality/valueThis study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

Journal

China Finance Review InternationalEmerald Publishing

Published: Jan 26, 2021

Keywords: Implied variance; Variance risk premium; Pricing errors; Kernel estimation; C50; C58

References