GARCHX-NoVaS: A Bootstrap-Based Approach of Forecasting for GARCHX Models

In this work, we explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transformation with the possible inclusion of exogenous variables in GARCH volatility specification. The NoVaS prediction method, which is inspired by a model-free prediction principle, has generally shown more accurate, stable and robust (to misspecifications) performance than that compared with classical GARCH-type methods. We derive the NoVaS transformation needed to include exogenous covariates and then construct the corresponding prediction procedure for multiple exogenous covariates. We address both point and interval forecasts using NoVaS type methods. We show through extensive simulation studies that bolster our claim that the NoVaS method outperforms traditional ones, especially for long-term time aggregated predictions. We also exhibit how our method could utilize geopolitical risks in forecasting volatility in national stock market indices. From an applied point-of-view for practitioners and policymakers, our methodology provides a distribution-free approach to forecast volatility and sheds light on how to leverage extra knowledge such as fundamentals- and sentiments-based information to improve the prediction accuracy of market volatility.