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

Author

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  • Adel Hassan A. Gadhi

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
    Institute of Public Administration, Riyadh 11141, Saudi Arabia)

  • Shelton Peiris

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia)

  • David E. Allen

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
    School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia
    Department of Finance, Asia University, Taichung 41354, Taiwan)

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.

Suggested Citation

  • Adel Hassan A. Gadhi & Shelton Peiris & David E. Allen, 2024. "Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN," JRFM, MDPI, vol. 17(9), pages 1-20, August.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:380-:d:1462741
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    References listed on IDEAS

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    Cited by:

    1. Adel Hassan A. Gadhi & Shelton Peiris & David E. Allen & Richard Hunt, 2025. "Optimal Time Series Forecasting Through the GARMA Model," Econometrics, MDPI, vol. 13(1), pages 1-23, January.

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