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

Adel Hassan A. Gadhi (), Shelton Peiris and David Allen
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Adel Hassan A. Gadhi: School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
Shelton Peiris: School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia

JRFM, 2024, vol. 17, issue 9, 1-20

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 (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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