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Can LSTM outperform volatility-econometric models?

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  • German Rodikov
  • Nino Antulov-Fantulin

Abstract

Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation. However, although many novel deep learning models were recently proposed, they still have a "hard time" surpassing strong econometric volatility models. Why is this the case? The volatility prediction task is of non-trivial complexity due to noise, market microstructure, heteroscedasticity, exogenous and asymmetric effect of news, and the presence of different time scales, among others. In this paper, we analyze the class of long short-term memory (LSTM) recurrent neural networks for the task of volatility prediction and compare it with strong volatility-econometric models.

Suggested Citation

  • German Rodikov & Nino Antulov-Fantulin, 2022. "Can LSTM outperform volatility-econometric models?," Papers 2202.11581, arXiv.org.
    • Handle: RePEc:arx:papers:2202.11581
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    References listed on IDEAS

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    Cited by:

    1. Werner Kristjanpoller, 2024. "A hybrid econometrics and machine learning based modeling of realized volatility of natural gas," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
    2. Zian Wang & Xinyi Lu, 2024. "COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning," Papers 2409.08356, arXiv.org.

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