Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1173-1187 |
| Number of pages | 15 |
| Journal | Journal of Business and Economic Statistics |
| Volume | 41 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2023 |
Bibliographical note
Funding Information:This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. We would like to thank the Associate Editor, two anonymous referees, Marcelo Fernandes, Patrick Gagliardini, René Garcia, Felipe Iachan, Alberto Ohashi, Ruy Ribeiro, conference participants at the 2013 First International Workshop in Financial Econometrics (Natal), 2014 Brazilian Meeting of Finance, 2014 Brazilian Meeting of Econometrics, 2015 SoFiE Conference (Aarhus), 2022 VieCo Conference (Copenhagen), 2022 SoFiE Conference (Cambridge) and seminar participants at the Financial Mathematics seminar at Princeton University, UFPE Department of Economics, and EMAp (Applied Math School - FGV) for useful comments and suggestions. This article is based on the second chapter of Gustavo Freire’s PhD Thesis at EPGE. Gustavo Freire is currently employed at Erasmus University Rotterdam.
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© 2022 American Statistical Association.