Robust estimation of risk‐neutral moments

This study provides an in‐depth analysis of how to estimate risk‐neutral moments robustly. A simulation and an empirical study show that estimating risk‐neutral moments presents a trade‐off between (a) the bias of estimates caused by a limited strike price domain and (b) the variance of estimates induced by microstructural noise. The best trade‐off is offered by option‐implied quantile moments estimated from a volatility surface interpolated with a local‐linear kernel regression and extrapolated linearly. A similarly good trade‐off is achieved by estimating regular central option‐implied moments from a volatility surface interpolated with a cubic smoothing spline and flat extrapolation.