Author Identifier

David E. Allen: https://orcid.org/0000-0001-7782-0865

Document Type

Journal Article

Publication Title

Journal of Risk and Financial Management

Volume

17

Issue

9

Publisher

MDPI

School

School of Business and Law

RAS ID

76014

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

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.

DOI

10.3390/jrfm17090380

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Link to publisher version (DOI)

10.3390/jrfm17090380