Option valuation under no-arbitrage constraints with neural networks

Yi Cao, Xiaoquan Liu, Jia Zhai

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)
12 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)361-374
Number of pages14
JournalEuropean Journal of Operational Research
Volume293
Issue number1
DOIs
Publication statusPublished - 16 Aug 2021

Keywords

  • Artificial neural networks
  • Finance
  • Hedging
  • Implied volatilities
  • Option greeks

ASJC Scopus subject areas

  • Computer Science (all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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