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Option Pricing Based on the Residual Neural Network

Author

Listed:
  • Lirong Gan

    (Guangdong University of Finance)

  • Wei-han Liu

    (Southern University of Science and Technology)

Abstract

We employ an innovative deep learning method to price options quickly and accurately. Specifically, we construct the Residual Neural Network model (ResNet) by two different basic residual blocks with three one-dimensional convolution layers and a shortcut. This model is a generalized option pricing method, and it can be used to approximate the option pricing formula without any assumptions. Besides, the model also can be easily extended to the deep ResNet model to achieve higher prediction accuracy. Comprehensive numerical experiments show that the deep ResNet model has excellent performance in the pricing of 50ETF options in the Chinese market, and the prediction accuracy of our model is higher than that of commonly used deep learning models, including deep neural network (DNN) and fully convolutional networks (FCN).

Suggested Citation

  • Lirong Gan & Wei-han Liu, 2024. "Option Pricing Based on the Residual Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1327-1347, April.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10413-3
    DOI: 10.1007/s10614-023-10413-3
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    References listed on IDEAS

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