Forecasting Realized Volatility of the Oil Future Prices Via Machine Learning
30 Pages Posted: 24 Nov 2020 Last revised: 3 Aug 2021
Date Written: October 8, 2020
This paper explores the possibility of the potential usage of machine learning models in the field of realized volatility forecasting of crude oil with a vast variety of empirical analyses and robustness checks. Although the conventional heterogeneous autoregressive (HAR) model is widely accepted, it failed in out-of-sample recently. However, machine learning models with the HAR factors can improve forecasting performance significantly. The model confidence set test, Diebold-Mariano test, and Pesaran-Timmermann test support the empirical results statistically.
Keywords: volatility forecasting, oil future, machine learning, forecasting model
JEL Classification: C5, C22, G1, Q4
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