Nonparametric Option Pricing with Generalized Entropic Estimators
提出一族仅需标的资产收益率数据、也可融入期权观测信息的非参数期权定价估计量，通过最小化不同Cressie-Read散度下的广义熵得到风险中性测度。应用于标普500和个股期权，含适度非线性的估计量定价精度优于Black-Scholes和GARCH模型，适合期权数据有限的情形。
We propose a family of nonparametric estimators for an option price that require only the use of underlying return data, but can also easily incorporate information from observed option prices. Each estimator comes from a risk-neutral measure minimizing generalized entropy according to a different Cressie–Read discrepancy. We apply our method to price S&P 500 options and the cross-section of individual equity options, using distinct amounts of option data in the estimation. Estimators incorporating mild nonlinearities produce optimal pricing accuracy within the Cressie–Read family and outperform several benchmarks such as Black–Scholes and different GARCH option pricing models. Overall, we provide a powerful option pricing technique suitable for scenarios of limited option data availability.