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- W3112507188 abstract "• We develop a gated neural network based option valuation model. • It satisfies no-arbitrage constraints and boundary conditions for European options. • A separate neural network is constructed to predict option-implied volatilities. • Empirically, the model beats popular alternatives in predicting option prices and hedging. In this paper, we start from the no-arbitrage constraints in option pricing and develop a novel hybrid gated neural network (hGNN) based option valuation model. We adopt a multiplicative structure of hidden layers to ensure model differentiability. We also select the slope and weights of input layers to satisfy the no-arbitrage constraints. Meanwhile, a separate neural network is constructed for predicting option-implied volatilities. Using S&P 500 options, our empirical analyses show that the hGNN model substantially outperforms well-established alternative models in the out-of-sample forecasting and hedging exercises. The superior prediction performance stems from our model’s ability in describing options on the boundary, and in offering analytical expressions for option Greeks which generate better hedging results." @default.
- W3112507188 created "2020-12-21" @default.
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- W3112507188 date "2021-08-01" @default.
- W3112507188 modified "2023-09-27" @default.
- W3112507188 title "Option valuation under no-arbitrage constraints with neural networks" @default.
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- W3112507188 doi "https://doi.org/10.1016/j.ejor.2020.12.003" @default.
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