GARHCX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables

Résumé 0

In this work, we further explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transformation after wrapping exogenous variables. In practice, especially in the area of financial econometrics, extra knowledge such as fundamentals- and sentiments-based information could be beneficial to improve the prediction accuracy of market volatility if they are incorporated into the forecasting process. In a classical approach, people usually apply GARCHX-type methods to include the exogenous variables. Being a Model-free prediction method, NoVaS has been shown to be more accurate and stable than classical GARCH-type methods. We are interested in whether the novel NoVaS method can also sustain its superiority after exogenous covariates are taken into account. We provide the NoVaS transformation based on GARCHX model and then claim the corresponding prediction procedure with exogenous variables existing. Also, simulation studies verify that the NoVaS method still outperforms traditional methods, especially for long-term time aggregated predictions.