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A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction

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  • Hu, Yan
  • Ni, Jian
  • Wen, Liu

Abstract

Forecasting the copper price volatility is an important yet challenging task. Given the nonlinear and time-varying characteristics of numerous factors affecting the copper price, we propose a novel hybrid method to forecast copper price volatility. Two important techniques are synthesized in this method. One is the classic GARCH model which encodes useful statistical information about the time-varying copper price volatility in a compact form via the GARCH forecasts. The other is the powerful deep neural network which combines the GARCH forecasts with both domestic and international market factors to search for better nonlinear features; it also combines the long short-term memory (LSTM) network with traditional artificial neural network (ANN) to generate better volatility forecasts. Our method synthesizes the merits of these two techniques and is especially suitable for the task of copper price volatility prediction. The empirical results show that the GARCH forecasts can serve as informative features to significantly increase the predictive power of the neural network model, and the integration of the LSTM and ANN networks is an effective approach to construct useful deep neural network structures to boost the prediction performance. Further, we conducted a series of sensitivity analyses of the neural network architecture to optimize the prediction results. The results suggest that the choice between LSTM and BLSTM networks for the hybrid model should consider the forecast horizon, while the ANN configurations should be fine-tuned depending on the choice of the measure of prediction errors.

Suggested Citation

  • Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
  • Handle: RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120304696
    DOI: 10.1016/j.physa.2020.124907
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    References listed on IDEAS

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    1. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, vol. 25(2), pages 259-281.
    2. Marcelo Sardelich & Suresh Manandhar, 2018. "Multimodal deep learning for short-term stock volatility prediction," Papers 1812.10479, arXiv.org.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
    5. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    6. Bentes, Sonia R., 2015. "Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: New evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 355-364.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    9. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    10. Lei Cui & Ke Huang & H.J. Cai, 2015. "Application of a TGARCH-wavelet neural network to arbitrage trading in the metal futures market in China," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 371-384, February.
    11. Ederington, Louis H & Lee, Jae Ha, 1993. "How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    12. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    13. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    14. Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
    15. Y. K. Tse, 1998. "The conditional heteroscedasticity of the yen-dollar exchange rate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 49-55.
    16. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
    17. Abdolreza Yazdani-Chamzini & Siamak Haji Yakhchali & Diana Volungevičienė & Edmundas Kazimieras Zavadskas, 2012. "Forecasting gold price changes by using adaptive network fuzzy inference system," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(5), pages 994-1010, April.
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