Informative option portfolios in filter design for option pricing models
研究了在期权定价模型中，如何通过构建低维度的期权组合滤波器来有效提取状态信息，并在定价和套期保值方面优于使用全部期权数据的方法，尤其适用于存在强偏态跳跃成分的模型。
Option pricing models are tools for pricing and hedging derivatives. Good models are complex and the econometrician faces many design decisions when bringing them to the data. I show that strategically constructed low-dimensional filter designs match and often outperform those that try to use all the available option data, in terms of state recovery, pricing, and hedging. The filters are built around option portfolios that aggregate option data, and track changes in risk-neutral variance and skewness. They also explicitly account for difficulties in the recovery of risk-neutral moments from option prices. The performance advantage is greatest in empirically relevant settings: in models with strongly skewed jump components that are not driven by Brownian volatility.