A1229
Title: Nonparametric risk-neutral density estimation using local cubic polynomials applied to intraday data
Authors: Antonio Santos - University of Coimbra (Portugal)
Ana Monteiro - University of Coimbra (Portugal) [presenting]
Abstract: A new approach is considered to estimate risk-neutral densities (RND) within a kernel regression framework, through local cubic polynomial estimation using intraday data. There is a new strategy for the definition of a criterion function used in nonparametric regression that includes calls, puts, and weights in the optimization problem associated with parameters estimation. No-arbitrage restrictions are incorporated in the problem through equality and bound constraints. This yields directly density functions of interest with minimum requirements needed. Within a simulation framework, it is demonstrated the robustness of proposed procedures. Additionally, RNDs are estimated through option prices associated with two indices, S\&P500 and VIX.
