Journal article
Forecasting realized volatility using HAR models and wavelet decomposition: A volatility-timing perspective
The North American Journal of Economics and Finance, Vol.83, pp.1-11
2026
Abstract
This study proposes a wavelet-based approach to forecasting Realized Volatility (RV) and evaluates its economic value within a volatility-timing framework. We apply wavelet decomposition to separate short-, medium-, and long-term components and generate forecasts using Heterogeneous Autoregressive (HAR) models. Forecasts based on the low-frequency component consistently lead to better portfolio outcomes, reducing turnover and enhancing investor utility without increasing risk. These results hold even when portfolio weights are forecast directly after being constructed from RV, or when jump-robust volatility estimates are used. The results highlight the importance of aligning forecast evaluation with practical investment objectives. Forecasts delivering the greatest welfare gains may not minimize conventional statistical loss functions.
Details
- Title
- Forecasting realized volatility using HAR models and wavelet decomposition: A volatility-timing perspective
- Authors
- Adam Clements (Corresponding Author) - Queensland University of TechnologyPuneet Vatsa - University of the Sunshine Coast, Queensland, School of Business and Creative Industries
- Publication details
- The North American Journal of Economics and Finance, Vol.83, pp.1-11
- Publisher
- Elsevier BV
- Date published
- 2026
- DOI
- 10.1016/j.najef.2026.102605
- ISSN
- 1879-0860
- Copyright note
- © 2026 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
- Data Availability
- Data will be made available on request.
- Organisation Unit
- School of Business and Creative Industries
- Language
- English
- Record Identifier
- 991210067502621
- Output Type
- Journal article
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