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

Autor:	Gadhi, Adel Hassan A., Peiris, Shelton, Allen, David E.
Předmět:	GENERATIVE adversarial networks PETROLEUM DEEP learning PETROLEUM sales & prices ECONOMETRIC models
Zdroj:	Journal of Risk & Financial Management; Sep2024, Vol. 17 Issue 9, p380, 20p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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