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On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models

Author

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  • Wenfeng Ma

    (School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
    These authors contributed equally to this work.)

  • Yuxuan Hong

    (College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China
    These authors contributed equally to this work.)

  • Yuping Song

    (School of Finance and Business, Shanghai Normal University, Shanghai 200234, China)

Abstract

Most of the deep-learning algorithms on stock price volatility prediction in the existing literature use data such as same-frequency market indicators or technical indicators, and less consider mixed-frequency data, such as macro-data. Compared with the traditional model that only inputs the same-frequency data such as technical indicators and market indicators, this study proposes an improved deep-learning model based on mixed-frequency big data. This paper first introduces the reserve restricted mixed-frequency data sampling (RR-MIDAS) model to deal with the mixed-frequency data and, secondly, extracts the temporal and spatial features of volatility series by using the parallel model of CNN-LSTM and LSTM, and finally utilizes the Optuna framework for hyper-parameter optimization to achieve volatility prediction. For the deep-learning model with mixed-frequency data, its RMSE, MAE, MSLE, MAPE, SMAPE, and QLIKE are reduced by 18.25%, 14.91%, 30.00%, 12.85%, 13.74%, and 23.42%, respectively. This paper provides a more accurate and robust method for forecasting the realized volatility of stock prices under mixed-frequency data.

Suggested Citation

  • Wenfeng Ma & Yuxuan Hong & Yuping Song, 2024. "On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models," Mathematics, MDPI, vol. 12(10), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1538-:d:1395182
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    References listed on IDEAS

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