Abstract
This paper examines the predictive ability of volatility in time series and investigates the effect of tradition learning methods blending with the Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Using Brent crude oil returns price volatility and environmental temperature for the city of Sydney in Australia, we have shown that the corresponding forecasts have improved when combined with WGAN-GP models (i.e., ANN-(WGAN-GP), LSTM-ANN-(WGAN-GP) and BLSTM-ANN (WGAN-GP)). As a result, we conclude that incorporating with WGAN-GP will’ significantly improve the capabilities of volatility forecasting in standard econometric models and deep learning techniques.
RAS ID
76014
Document Type
Journal Article
Date of Publication
9-1-2024
Volume
17
Issue
9
School
School of Business and Law
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
MDPI
Identifier
David E. Allen: https://orcid.org/0000-0001-7782-0865
Recommended Citation
Gadhi, A. A., Peiris, S., & Allen, D. E. (2024). Improving volatility forecasting: A study through hybrid deep Learning methods with WGAN. DOI: https://doi.org/10.3390/jrfm17090380
Comments
Gadhi, A. H. A., Peiris, S., & Allen, D. E. (2024). Improving volatility forecasting: A study through hybrid deep learning methods with WGAN. Journal of Risk & Financial Management, 17(9). https://doi.org/10.3390/jrfm17090380