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Article

Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN

by
Adel Hassan A. Gadhi
1,2,*,
Shelton Peiris
1 and
David E. Allen
1,3,4
1
School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
2
Institute of Public Administration, Riyadh 11141, Saudi Arabia
3
School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia
4
Department of Finance, Asia University, Taichung 41354, Taiwan
*
Author to whom correspondence should be addressed.
J. Risk Financ. Manag. 2024, 17(9), 380; https://doi.org/10.3390/jrfm17090380
Submission received: 9 July 2024 / Revised: 14 August 2024 / Accepted: 15 August 2024 / Published: 23 August 2024
(This article belongs to the Section Financial Markets)
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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.
Keywords: forecasting; volatility; GARCH-ANN; GARCH-LSTM-ANN; WGAN; gradient penalty; GARCH-BLSTM-ANN; hybrid oil price forecasting; volatility; GARCH-ANN; GARCH-LSTM-ANN; WGAN; gradient penalty; GARCH-BLSTM-ANN; hybrid oil price

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MDPI and ACS Style

Gadhi, A.H.A.; Peiris, S.; Allen, D.E. Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN. J. Risk Financ. Manag. 2024, 17, 380. https://doi.org/10.3390/jrfm17090380

AMA Style

Gadhi AHA, Peiris S, Allen DE. Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN. Journal of Risk and Financial Management. 2024; 17(9):380. https://doi.org/10.3390/jrfm17090380

Chicago/Turabian Style

Gadhi, Adel Hassan A., Shelton Peiris, and David E. Allen. 2024. "Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN" Journal of Risk and Financial Management 17, no. 9: 380. https://doi.org/10.3390/jrfm17090380

APA Style

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 and Financial Management, 17(9), 380. https://doi.org/10.3390/jrfm17090380

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