Volatility forecasting with machine learning and intraday commonality

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.

Financial Time Series Forecasting: A Data Stream Mining-Based System

“…Data science and financial economics-oriented agents can work together for more profitable approaches and better forecasting systems. In this context, we cite [50], where authors used machine learning to predict intra-day realized volatility. The study considered stock market crashes such as the European debt crisis, the China-United States trade war, and COVID-19.…”