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

Wenfeng Ma, Yuxuan Hong and Yuping Song ()
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Wenfeng Ma: School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
Yuxuan Hong: College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China
Yuping Song: School of Finance and Business, Shanghai Normal University, Shanghai 200234, China

Mathematics, 2024, vol. 12, issue 10, 1-21

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.

Keywords: mixed frequency data; RR-MIDAS model; volatility prediction; deep learning; Optuna framework (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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