Forecasting Stock Price Index Volatility with LSTM Deep Neural Network

ShuiLing Yu and Zhe Li ()
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ShuiLing Yu: School of Science, Changchun University of Science and Technology
Zhe Li: School of Science, Changchun University of Science and Technology

Chapter Chapter 29 in Recent Developments in Data Science and Business Analytics, 2018, pp 265-272 from Springer

Abstract: 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.

Keywords: LSTM; Volatility forecasting; Extreme value volatility; GARCH (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-72745-5_29

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DOI: 10.1007/978-3-319-72745-5_29

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