AI 中文总结
提出独特参数化风险中性分布方法，用简约可解释参数控制隐含波动率曲线形状，能捕捉多种形状。通过标准普尔500指数期权数据集实证，校准准确，拟合参数稳定，可进行期限结构插值和动态过程构建。
AI 中文摘要
我们提出了一种独特的参数化风险中性分布的方法。该模型使用简约且可解释的参数，能对隐含波动率曲线的形状进行直接和局部控制，可捕捉多种形状，包括局部凹形。实证结果表明，在两年期标准普尔500指数期权数据集中的25万条曲线校准准确。拟合参数在期限上呈现稳定模式，可进行期限结构插值和动态过程构建且无静态套利。
英文摘要
We present a distinctive approach to parameterizing the risk neutral distribution. Using parsimonious and interpretable parameters, the model provides direct and localized control over the shape of the implied volatility curve. It captures a wide variety of shapes, including those with local concavity. Empirical results demonstrate accurate calibration across a quarter million curves from a two-year Standard and Poor's 500 index option dataset. The fitted parameters exhibit stable patterns across tenors, enabling term structure interpolation and dynamic process construction without static arbitrage.
