Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288886 
Year of Publication: 
2020
Citation: 
[Journal:] Review of Derivatives Research [ISSN:] 1573-7144 [Volume:] 23 [Issue:] 3 [Publisher:] Springer US [Place:] New York, NY [Year:] 2020 [Pages:] 323-355
Publisher: 
Springer US, New York, NY
Abstract: 
We find that option-implied information such as forward-looking variance, skewness and the variance risk premium are sensitive to the way the volatility surface is constructed. For some state-of-the-art volatility surfaces, the differences are economically surprisingly large and lead to systematic biases, especially for out-of-the-money put options. Estimates for risk-neutral variance differ across volatility surfaces by more than 10% on average, leading to variance risk premium estimates that differ by 60% on average. The variations are even larger for risk-neutral skewness. To overcome this problem, we propose a volatility surface that is built with a one-dimensional kernel regression. We assess its statistical accuracy relative to existing state-of-the-art parametric, semi- and non-parametric volatility surfaces by means of leave-one-out cross-validation, including the volatility surface of OptionMetrics. Based on 14 years of end-of-day and intraday S&P 500 and Euro Stoxx 50 option data we conclude that the proposed one-dimensional kernel regression represents option market information more accurately than existing approaches of the literature.
Subjects: 
Option-implied
Risk-neutral variance
Risk-neutral density
Tail risk
Option standardization
Interpolation
JEL: 
G13
G17
C14
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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