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.

考虑采用一种新方法，通过使用日内数据进行局部三次多项式估计来估计核回归框架内的风险中性密度（RND）。非参数回归中使用的标准函数定义有一种新策略，其中包括与参数估计相关的优化问题中的看涨期权、看跌期权和权重。通过等式和有界约束将无套利约束纳入问题中。所考虑的方法以所需的最低要求直接产生感兴趣的密度函数。在模拟框架内，证明了所提出的程序的稳健性。此外，RND 是通过与 S&P500 和 VIX 这两个指数相关的期权价格来估计的。