Volatility Forecasting with Machine Learning and Intraday Commonality*

Chao Zhang, Yihuang Zhang, Mihai Cucuringu and Zhongmin Qian

Journal of Financial Econometrics, 2024, vol. 22, issue 2, 492-530

Abstract: We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.

Keywords: commonality; intraday volatility forecasting; neural networks; realized volatility (search for similar items in EconPapers)
JEL-codes: C45 C53 G17 (search for similar items in EconPapers)
Date: 2024
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