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

• Autor(a) principal: Monteiro, Ana Margarida Machado
• Data de Publicação: 2019
• Outros Autores: Santos, António Alberto Ferreira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/87034
• topic: kernel functions, Local polynomials, No-arbitrage constraints, Option prices, Risk-neutral density

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.