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3D Tensor-based Deep Learning Models for Predicting Option Price

Author

Listed:
  • Muyang Ge
  • Shen Zhou
  • Shijun Luo
  • Boping Tian
Abstract
Option pricing is a significant problem for option risk management and trading. In this article, we utilize a framework to present financial data from different sources. The data is processed and represented in a form of 2D tensors in three channels. Furthermore, we propose two deep learning models that can deal with 3D tensor data. Experiments performed on the Chinese market option dataset prove the practicability of the proposed strategies over commonly used ways, including B-S model and vector-based LSTM.

Suggested Citation

  • Muyang Ge & Shen Zhou & Shijun Luo & Boping Tian, 2021. "3D Tensor-based Deep Learning Models for Predicting Option Price," Papers 2106.02916, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2106.02916
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    References listed on IDEAS

    as
    1. A Itkin, 2019. "Deep learning calibration of option pricing models: some pitfalls and solutions," Papers 1906.03507, arXiv.org.
    2. Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
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    Cited by:

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    2. Devi Munandar & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Hilman Ferdinandus Pardede, 2023. "Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-25, July.

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