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

Creative Commons Attribution 4.0 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

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

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Link to publisher version (DOI)

10.3390/jrfm17090380