  We investigate the predictive abilities of the heterogeneous autoregressive
(HAR) model compared to machine learning (ML) techniques across an
unprecedented dataset of 1,455 stocks. Our analysis focuses on the role of
fitting schemes, particularly the training window and re-estimation frequency,
in determining the HAR model's performance. Despite extensive hyperparameter
tuning, ML models fail to surpass the linear benchmark set by HAR when
utilizing a refined fitting approach for the latter. Moreover, the simplicity
of HAR allows for an interpretable model with drastically lower computational
costs. We assess performance using QLIKE, MSE, and realized utility metrics,
finding that HAR consistently outperforms its ML counterparts when both rely
solely on realized volatility and VIX as predictors. Our results underscore the
importance of a correctly specified fitting scheme. They suggest that properly
fitted HAR models provide superior forecasting accuracy, establishing robust
guidelines for their practical application and use as a benchmark. This study
not only reaffirms the efficacy of the HAR model but also provides a critical
perspective on the practical limitations of ML approaches in realized
volatility forecasting.
