Forecasting realized volatility with machine learning: Panel data perspective

Machine learning approaches have become very popular in many fields in this big data age. This paper considers the problem of forecasting realized volatility with machine learning using high-frequency data. Instead of treating the realized volatility as a univariate time series studied by many existing works in literature, we employ panel data analysis to improve forecasting accuracy in the short term. We use six effective machine-learning methods for the realized volatility panel data. We compare our results with the traditional linear-type models under the same panel data framework and with the single time series forecasting via the same machine learning methods. The results show that the panel-data-based machine learning method (PDML) outperforms the other methods.