Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN
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...
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| Veröffentlicht in: | Journal of risk and financial management 2024-09, Vol.17 (9), p.380 |
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| container_title | Journal of risk and financial management |
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| creator | Gadhi, Adel Hassan A. Peiris, Shelton Allen, David E. |
| description | 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. |
| doi_str_mv | 10.3390/jrfm17090380 |
| format | Article |
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| ispartof | Journal of risk and financial management, 2024-09, Vol.17 (9), p.380 |
| issn | 1911-8074 1911-8066 1911-8074 |
| language | eng |
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| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Accuracy Analysis Decision trees Deep learning Econometric models Economic forecasting Forecasting Global economy Machine learning Mean square errors Methods Neural networks Securities markets Stochastic models Time series Volatility |
| title | Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN |
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