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

Creative Commons Attribution 4.0 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

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Link to publisher version (DOI)

10.3390/jrfm17090380