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Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities

Abstract : Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices.
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https://hal.umontpellier.fr/hal-02921304
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Soumis le : mercredi 26 août 2020 - 09:55:12
Dernière modification le : jeudi 19 mai 2022 - 16:04:06

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Distributed under a Creative Commons Paternité 4.0 International License

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Jules Sadefo-Kamdem, Rose Bandolo Essomba, James Njong Berinyuy. Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities. Chaos, Solitons and Fractals, Elsevier, 2020, 140, pp.110215. ⟨10.1016/j.chaos.2020.110215⟩. ⟨hal-02921304⟩

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