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Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction

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  • Yu Zheng
  • Yongxin Yang
  • Bowei Chen

Abstract

In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, financial conditions, such as the absence of arbitrage, the boundaries and the asymptotic slope, are embedded into the loss function. This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training. The proposed model outperforms the benchmarked models with the option data on the S&P 500 index over 20 years. More importantly, the domain knowledge is satisfied empirically, showing the model is consistent with the existing financial theories and conditions related to implied volatility surface.

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  • Yu Zheng & Yongxin Yang & Bowei Chen, 2019. "Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction," Papers 1904.12834, arXiv.org, revised May 2021.
    • Handle: RePEc:arx:papers:1904.12834
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    File URL: http://arxiv.org/pdf/1904.12834
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    Cited by:

    1. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.

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