Option valuation under no-arbitrage constraints with neural networks
Xiaoquan Liu and
European Journal of Operational Research, 2021, vol. 293, issue 1, 361-374
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.
Keywords: Finance; Artificial neural networks; Implied volatilities; Option greeks; Hedging (search for similar items in EconPapers)
JEL-codes: C63 F47 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:293:y:2021:i:1:p:361-374
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