Nonparametric Option Pricing with Generalized Entropic Estimators

We propose a family of nonparametric estimators for an option price that require only the use underlying return data, but can also easily incorporate information from observed prices. Each estimator comes risk-neutral measure minimizing generalized entropy according to different Cressie–Read discrepancy. apply our method S&P 500 options and cross-section individual equity options, using distinct amounts data in estimation. Estimators incorporating mild nonlinearities produce optimal pricing accuracy within outperform several benchmarks such as Black–Scholes GARCH models. Overall, we provide powerful technique suitable scenarios limited availability.