Francesco Audrino, Jonathan Chassot
arXiv 12 Jun 2024 · Finance — Statistical Finance · publishedInternational Journal of Forecasting (2025) · 3 citations (OpenAlex)
arXiv:2406.08041 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Audrino, F., Sigrist, F., & Ballinari, D (2020) The impact of sentiment and attention measures on stock market volatility self | 1.000 | 11 | 4 | 100% |
| 2 | Zhang, C., Zhang, Y., Cucuringu, M., & Qian, Z (2023) Volatility Forecasting with Machine Learning and Intraday Commonality | 0.983 | 20 | 6 | 95% |
| 3 | Christensen, K., Siggaard, M., & Veliyev, B (2023) A Machine Learning Approach to Volatility Forecasting | 0.981 | 18 | 5 | 94% |
| 4 | Audrino, F., & Knaus, S. D (2016) Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics self | 0.928 | 4 | 3 | 100% |
| 5 | Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H (2018) Risk Everywhere: Modeling and managing volatility | 0.874 | 9 | 2 | 100% |
| 6 | Buncic, D., & Gisler, K. I (2016) Global equity market volatility spillovers: A broader role for the United States | 0.843 | 3 | 3 | 100% |
| 7 | Clements, A., & Preve, D. P (2021) A Practical Guide to harnessing the HAR volatility model | 0.843 | 3 | 3 | 100% |
| 8 | Zhang, Y., Ma, F., & Liao, Y (2020) Forecasting global equity market volatilities | 0.843 | 3 | 3 | 100% |
| 9 | Qiu, Y (2021) Complete subset least squares support vector regression | 0.737 | 3 | 2 | 100% |
| 10 | Hastie, T., Tibshirani, R., & Friedman, J (2009) The Elements of Statistical Learning | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 38 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Sparse Tree-Based Aggregation for Time Series Regressions | 0.737 | 3 | 2 |
| 2 | Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions | 0.405 | 1 | 1 |