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Modeling financial time-series with generative adversarial networks

Author

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  • Takahashi, Shuntaro
  • Chen, Yu
  • Tanaka-Ishii, Kumiko

Abstract

Financial time-series modeling is a challenging problem as it retains various complex statistical properties and the mechanism behind the process is unrevealed to a large extent. In this paper, a deep neural networks based approach, generative adversarial networks (GANs) for financial time-series modeling is presented. GANs learn the properties of data and generate realistic data in a data-driven manner. The GAN model produces a time-series that recovers the statistical properties of financial time-series such as the linear unpredictability, the heavy-tailed price return distribution, volatility clustering, leverage effects, the coarse-fine volatility correlation, and the gain/loss asymmetry.

Suggested Citation

  • Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307277
    DOI: 10.1016/j.physa.2019.121261
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