Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/87034
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. | URI: | https://hdl.handle.net/10316/87034 | Rights: | openAccess |
Appears in Collections: | I&D CeBER - Working Papers |
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File | Description | Size | Format | |
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Working Paper.pdf | 1.45 MB | Adobe PDF | View/Open |
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