Machine Learning and HAR Models for Realised Volatility Forecasting. An Application in Brent Crude Front Month Futures Market

As Volatility forecasting is critical for risk management and speculative trading, the thesis investigates the application of Machine Learning and Heterogeneous Autoregressive (HAR) models to forecast realised volatility in the highly volatile Brent Crude Oil market. The study evaluates whether ML models outperform traditional HAR models in forecasting Realised Volatility, a volatility estimator based on high-frequency data, by testing both approaches across multiple forecast horizons: one day, one week, and one month.