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.

中文翻译：

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通过无多项套利约束的局部多项式核平滑从期权价格获得的条件风险中性密度

通过使用日内数据通过局部三次多项式估计，可以考虑采用一种新方法来估计内核回归框架内的风险中性密度（RND）。有一种用于定义非参数回归中使用的标准函数的新策略，该策略包括与参数估计相关的优化问题中的调用，放置和权重。无套利约束通过相等性和约束性约束纳入问题。所考虑的方法可以直接产生感兴趣的密度函数，而所需的最小需求却很少。在仿真框架内，证明了所提出程序的鲁棒性。此外，RND通过与两个指数S＆P500和VIX相关的期权价格进行估算。