Exploring extension of HAR volatility prediction
This paper proposes the HAR-weighted model, introducing an inverse standard deviation weighting scheme to the HAR-RV framework - a methodological innovation previously unexplored in the volatility forecasting literature. Our approach systematically mitigates the model’s sensitivity to high-volatilit...
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| 主要作者: | |
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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2025
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/184491 |
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| 總結: | This paper proposes the HAR-weighted model, introducing an inverse standard deviation weighting scheme to the HAR-RV framework - a methodological innovation previously unexplored in the volatility forecasting literature. Our approach systematically mitigates the model’s sensitivity to high-volatility periods through variance-adaptive weighting while preserving the interpretability of the original specification. Theoretically, we establish the first
formal asymptotic theory for HAR-type estimators under Elastic Net Regularization, resolving important open questions in robust volatility estimation. The model further enhances predictive performance through judiciously designed non-linear transformations. Comprehensive empirical analysis demonstrates consistent outperformance relative to benchmark specifications, which can be applied in the field of Value-at-Risk. This work both advances the methodological frontier of realized volatility modeling and delivers practical improvements for financial risk management. |
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