JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (9): 96-107.doi: 10.6040/j.issn.1671-9352.0.2025.119

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Volatility forecasting of stock market based on deep learning model with mixed-frequency data

ZHANG Yufeng, ZHANG Tonghui*, WANG Yuanyue   

  1. School of Economics, Ocean University of China, Qingdao 266100, Shandong, China
  • Published:2026-09-30

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Abstract: To address challenges in stock market volatility prediction, such as multiple influencing factors, mixed data frequencies, and nonlinear relationships, the Shanghai Composite Index is selected as the modeling target, and a hybrid framework is constructed by combining the GARCH-MIDAS model with the CNN-BiLSTM-Attention deep learning model. Monthly macroeconomic indicators, daily economic policy uncertainty, and technical trading indicators are introduced as predictive variables. Empirical results show that the CNN-BiLSTM-Attention model outperforms benchmark models, achieving the lowest out-of-sample prediction loss. In particular, its mean squared error is reduced by 47 percent compared to the LSTM model. The inclusion of macroeconomic and other predictors also provides valuable information, enhancing forecasting performance.

Key words: volatility forecasting, macroeconomics, GARCH-MIDAS model, CNN-BiLSTM-Attention model

CLC Number: 

  • O211
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