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Conditional risk-neutral density from option prices by local polynomial kernel smoothing with no-arbitrage constraints

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  • Ana M. Monteiro

    (University of Coimbra)

  • Antonio A. F. Santos

    (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 constraints are incorporated into the problem through equality and bound constraints. The approach considered 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 M. Monteiro & Antonio A. F. Santos, 2020. "Conditional risk-neutral density from option prices by local polynomial kernel smoothing with no-arbitrage constraints," Review of Derivatives Research, Springer, vol. 23(1), pages 41-61, April.
    • Handle: RePEc:kap:revdev:v:23:y:2020:i:1:d:10.1007_s11147-019-09156-x
    DOI: 10.1007/s11147-019-09156-x
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    References listed on IDEAS

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