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Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models

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Time Added
2022/12/12 19:33
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Deep Learning
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Authors
Yuping Song Xiaolong Tang Hemin Wang and Zhiren Ma
Abstract
Empirical experiments have shown that macroeconomic variables can affect the volatility of stock market. However the frequencies of macroeconomic variables are low and different from the stock market volatility and few literature considers the low‐frequency macroeconomic variables as input indicators for deep learning models. In this paper we forecast the stock market volatility incorporating low‐frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH‐MIDAS) model to process the mixing frequency data. This paper firstly takes macroeconomic variables as exogenous variables then uses the GARCH‐MIDAS model to deal with the problem of different frequencies between the macroeconomic variables and stock market volatility and to forecast the short‐term volatility and finally takes the predicted short‐term volatility as the input indicator into machine learning and deep learning models to forecast the realized volatility of stock market. It is found that adding macroeconomic variables can significantly improve the forecasting ability in the comparison of the forecasting effects of the same model before and after adding the macroeconomic variables. Additionally in the comparison of the forecasting effects among different models it is also found that the forecasting effect of the deep learning model is the best the machine learning model is worse and the
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Year Published
2023
Series
Journal of Forecasting 2023 vol. 42 issue 1 51-59
Rank
0.91
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