Author Identifier (ORCID)
David E. Allen: https://orcid.org/0000-0001-7782-0865
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.
Document Type
Journal Article
Date of Publication
9-1-2024
Volume
17
Issue
9
Publication Title
Journal of Risk and Financial Management
Publisher
MDPI
School
School of Business and Law
RAS ID
76014
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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