Kernel density estimation using local cubic polynomials through option prices applied to intraday data

Ana Margarida Monteiro () and Antonio Santos ()
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Ana Margarida Monteiro: Faculty of Economics, Centre for Business and Economics Research (CeBER), Monetary and Financial Research Group (GEMF), University of Coimbra

No 2019-02, CeBER Working Papers from Centre for Business and Economics Research (CeBER), University of Coimbra

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.

Keywords: : kernel functions; Local polynomials; No-arbitrage constraints; Option prices; Risk-neutral density. (search for similar items in EconPapers)
JEL-codes: C14 C15 C61 G13 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2019-02
New Economics Papers: this item is included in nep-ore
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