Conditional risk-neutral density from option prices by local polynomial kernel smoothing with no-arbitrage constraints
Ana M. Monteiro () and
Antonio A. F. Santos ()
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Ana M. Monteiro: University of Coimbra
Antonio A. F. Santos: University of Coimbra
Review of Derivatives Research, 2020, vol. 23, issue 1, No 2, 61 pages
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.
Keywords: Kernel functions; Local polynomials; No-arbitrage constraints; Option prices; Risk-neutral density; C14; C15; C61; G13 (search for similar items in EconPapers)
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