A1413
Title: Realized volatility forecasting using extreme gradient boosting
Authors: Andreas Teller - Friedrich Schiller University Jena (Germany) [presenting]
Uta Pigorsch - University of Wuppertal (Germany)
Christian Pigorsch - Friedrich Schiller University Jena (Germany)
Abstract: Extreme Gradient Boosting (XGBoost) is adopted to forecast realized volatility. This is motivated by XGBoost's strong forecasting performance in other forecast applications and its ability to capture non-linearities, a feature that is also oftentimes reported in the context of realized volatility. We examine the forecasting precision of linear and non-linear XGBoost models for different forecast horizons and compare it to that of Long Short-Term Memory (LSTM) networks as well as heterogeneous autoregressive (HAR) models. We find that XGBoost exhibits a better forecast performance. In particular, XGBoost models significantly outperform both HAR and LSTM models for one-step-ahead predictions. For longer forecast horizons, linear models such as XGBoost with linear base learners perform better than non-linear specifications, suggesting that accounting for non-linearities is only important if short-term forecasts are of interest.
