EconPapers    
Economics at your fingertips  
 

Volatility forecasting with machine learning and intraday commonality

Chao Zhang, Yihuang Zhang, Mihai Cucuringu and Zhongmin Qian

Papers from arXiv.org

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 one-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.

Date: 2022-02, Revised 2023-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ecm, nep-fmk, nep-for, nep-mst and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://arxiv.org/pdf/2202.08962 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2202.08962

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2024-12-28
Handle: RePEc:arx:papers:2202.08962