Volatility Forecasting with Machine Learning and Intraday Commonality

Journal of Financial Econometrics, 2024, Vol. 22, No. 2, 492–530

44 Pages Posted: 18 Mar 2022 Last revised: 20 Apr 2024

See all articles by Chao Zhang

Chao Zhang

University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Yihuang Zhang

University of Oxford - Mathematical Institute

Mihai Cucuringu

University of Oxford - Department of Statistics; The Alan Turing Institute

Zhongmin Qian

Mathematical Institute

Date Written: May 1, 2022

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 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 one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting diurnal 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: Intraday volatility forecasting, Neural networks, Realized volatility, Commonality

JEL Classification: C45, C53, G17

Suggested Citation

Zhang, Chao and Zhang, Yihuang and Cucuringu, Mihai and Qian, Zhongmin, Volatility Forecasting with Machine Learning and Intraday Commonality (May 1, 2022). Journal of Financial Econometrics, 2024, Vol. 22, No. 2, 492–530, Available at SSRN: https://ssrn.com/abstract=4022147 or http://dx.doi.org/10.2139/ssrn.4022147

Chao Zhang (Contact Author)

University of Oxford ( email )

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Yihuang Zhang

University of Oxford - Mathematical Institute ( email )

Radcliffe Observatory, Andrew Wiles Building
Woodstock Rd
Oxford, Oxfordshire OX2 6GG
United Kingdom

Mihai Cucuringu

University of Oxford - Department of Statistics ( email )

24-29 St Giles
Oxford
United Kingdom

HOME PAGE: http://https://www.stats.ox.ac.uk/~cucuring/

The Alan Turing Institute ( email )

British Library, 96 Euston Road
96 Euston Road
London, NW12DB
United Kingdom

Zhongmin Qian

Mathematical Institute ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

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