Abstract
In this work, we explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transforma-tion with the possible inclusion of exogenous variables in GARCH volatility specification. The NoVaS prediction method, whichis 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 includeexogenous covariates and then construct the corresponding prediction procedure for multiple exogenous covariates. We addressboth point and interval forecasts using NoVaS type methods. We show through extensive simulation studies that bolster ourclaim that the NoVaS method outperforms traditional ones, especially for long-term time aggregated predictions. We also exhibithow 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 shedslight on how to leverage extra knowledge such as fundamentals- and sentiments-based information to improve the predictionaccuracy of market volatility.