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DeepVol: A Pre-Trained Universal Asset Volatility Model

Author

Listed:
  • Chen Liu
  • Minh-Ngoc Tran
  • Chao Wang
  • Richard Gerlach
  • Robert Kohn

Abstract

This paper introduces DeepVol, a pre-trained deep learning volatility model that is more general than traditional econometric models. DeepVol leverage the power of transfer learning to effectively capture and model the volatility dynamics of all financial assets, including previously unseen ones, using a single universal model. This contrasts to the usual practice in the econometrics literature, which trains a separate model for each asset. The introduction of DeepVol opens up new avenues for volatility modeling in the finance industry, potentially transforming the way volatility is predicted.

Suggested Citation

  • Chen Liu & Minh-Ngoc Tran & Chao Wang & Richard Gerlach & Robert Kohn, 2023. "DeepVol: A Pre-Trained Universal Asset Volatility Model," Papers 2309.02072, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2309.02072
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    References listed on IDEAS

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