Comparison of Methods to Estimate Option Implied Risk Neutral Densities

This paper is a comparison study of non-parametric techniques used to estimate risk neutral densities from option prices. Cross sectional option prices are first generated using Monte Carlo simulation. Using these simulated options data, risk neutral densities of the underlying asset are estimated using three different non-parametric methods. The performance of these non-parametric estimation methods are then evaluated by comparing the estimated densities to the theoretical density. Unlike previous comparison studies that use traded options data without knowing the true risk neutral densities, this study uses simulated option data with known data generating processes and their corresponding risk neutral densities, hence giving a real evaluation on the non-parametric estimation methods. This study finds that the kernel regression method yields the best performance, followed by the spline interpolation method and the neural network models