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...
محفوظ في:
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/184491 |
| الوسوم: |
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | 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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