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

Author

Listed:
  • Ana Margarida Monteiro

    (Faculty of Economics, Centre for Business and Economics Research (CeBER), Monetary and Financial Research Group (GEMF), University of Coimbra)

  • António Alberto Ferreira Santos

    (Faculty of Economics, Centre for Business and Economics Research (CeBER), Monetary and Financial Research Group (GEMF), 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.

Suggested Citation

  • Ana Margarida Monteiro & António Alberto Ferreira Santos, 2019. "Kernel density estimation using local cubic polynomials through option prices applied to intraday data," CeBER Working Papers 2019-02, Centre for Business and Economics Research (CeBER), University of Coimbra.
    • Handle: RePEc:gmf:papers:2019-02
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    More about this item

    Keywords

    : kernel functions; Local polynomials; No-arbitrage constraints; Option prices; Risk-neutral density.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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