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Title: Kernel density estimation using local cubic polynomials through option prices applied to intraday data
Authors: Monteiro, Ana Margarida Machado 
Santos, António Alberto Ferreira 
Keywords: kernel functions, Local polynomials, No-arbitrage constraints, Option prices, Risk-neutral density
Issue Date: 28-Feb-2019
Series/Report no.: CeBeR Working Paper 2019-02;;
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.
Rights: openAccess
Appears in Collections:I&D CeBER - Working Papers

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