Stock market volatility prediction: Evidence from a new bagging model

Qin Luo, Jinfeng Bu, Weiju Xu and Dengshi Huang

International Review of Economics & Finance, 2023, vol. 87, issue C, 445-456

Abstract: The purpose of this study is to investigate which model can improve the precision of the categorical economic policy uncertainty indices in predicting volatility in the U.S. stock market. In this study, a new model is constructed by combining autoregressive model and bagging method. The empirical outcomes indicate that machine learning models outperform traditional forecasting models and that the new model constructed in this study has the best forecasting ability. We perform robustness tests using an alternative stock index, alternative forecasting windows, and different economic cycles. The results show that these findings are robust. We hope to provide new insights into the application of the bagging method in stock market volatility forecasting.

Keywords: Bagging; Volatility forecasting; Categorical EPU indices; U.S. stock market (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:87:y:2023:i:c:p:445-456

DOI: 10.1016/j.iref.2023.05.008

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