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Forecasting Stock Price Index Volatility with LSTM Deep Neural Network

In: Recent Developments in Data Science and Business Analytics

Author

Listed:
  • ShuiLing Yu

    (School of Science, Changchun University of Science and Technology)

  • Zhe Li

    (School of Science, Changchun University of Science and Technology)

Abstract

In strong noisy financial market, accurate volatility forecasting is the core task in risk management. In this paper, we apply GARCH model and a LSTM model to predict the stock index volatility. Instead of historical volatility, we select extreme value volatility of Shanghai Compos stock price index to conduct empirical study. By comparing the values of four types of loss functions, we illustrate that LSTM model has a better predicting effect.

Suggested Citation

  • ShuiLing Yu & Zhe Li, 2018. "Forecasting Stock Price Index Volatility with LSTM Deep Neural Network," Springer Proceedings in Business and Economics, in: Madjid Tavana & Srikanta Patnaik (ed.), Recent Developments in Data Science and Business Analytics, chapter 0, pages 265-272, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-72745-5_29
    DOI: 10.1007/978-3-319-72745-5_29
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    Citations

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    Cited by:

    1. Peiwan Wang & Lu Zong & Ye Ma, 2019. "An Integrated Early Warning System for Stock Market Turbulence," Papers 1911.12596, arXiv.org.
    2. Ji‐Eun Choi & Dong Wan Shin, 2022. "Parallel architecture of CNN‐bidirectional LSTMs for implied volatility forecast," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1087-1098, September.
    3. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.

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