Predicting directional volatility: HAR model with machine learning integration

Abstract

ABSTRACT Empirical studies on volatility forecasting have predominantly concentrated on point or interval estimates. However, the study of directional changes in volatility remains a relatively underexplored domain. Prediction of directions in volatility is particularly important for timing strategies and asset allocation. In this paper, we consider the predictability of both short-term and long-term volatility directions using a range of machine learning techniques integrating a parsimonious Heterogeneous Autoregressive (HAR) structure. An empirical analysis was undertaken using the S&P500’s realized volatility and the Chicago Board Options Exchange’s (CBOE) VIX implied volatility. We show that the machine learning techniques consistently enhance the accuracy of volatility directional forecasts. Among these techniques, the Support Vector Machine (SVM) stands out by consistently achieving significant forecasting accuracy and the most substantial economic gains.