Forecasting Stock Market Volatility Using CNN-BiLSTM-Attention Model with Mixed-Frequency Data

Forecasting Stock Market Volatility Using CNN-BiLSTM-Attention Model with Mixed-Frequency Data

Existing stock volatility forecasting models predominantly rely on same-frequency market data while neglecting mixed-frequency integration and face particular challenges in incorporating low-frequency macroeconomic variables that exhibit temporal mismatches with financial market dynamics. To address...

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Bibliographic Details
Main Authors: Yufeng Zhang, Tonghui Zhang, Jingyi Hu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Mathematics
Subjects:
macroeconomic variables
volatility forecasting
GARCH-MIDAS model
deep learning model
Online Access:https://www.mdpi.com/2227-7390/13/11/1889
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