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.

中文翻译：

---

期权隐含信息：vol曲面与它有什么关系？

我们发现，期权隐含信息（例如前瞻性方差，偏度和方差风险溢价）对波动率表面的构造方式很敏感。对于某些最新的波动率面，差异在经济上出乎意料地大，并导致系统性偏差，尤其是对于价外的看跌期权。风险中性方差的估计值在整个波动表面上平均相差10％以上，导致方差风险溢价估计值平均相差60％。对于风险中性偏斜，差异甚至更大。为了克服这个问题，我们提出了使用一维内核回归构建的波动表面。我们会评估其相对于现有最新参数的统计准确性，通过留一法交叉验证的半和非参数波动率面，包括OptionMetrics的波动率面。基于14年的日末和日间标普500指数和Euro Stoxx 50期权数据，我们得出的结论是，与现有文献方法相比，拟议的一维核回归更准确地表示了期权市场信息。