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Abstract
Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices.
Highlights
The petroleum market is currently going through one of the most volatile times in its history.The volatilities of crude oil prices are affected by macroeconomic and microeconomic variables and by the speculative activities and non-economic variables such as geopolitical tensions, theGulf war, and, nowadays, the coronavirus disease 2019 (COVID-19) and the conflict between Russia and Saudi Arabia
In the process of the estimation of the GARCHLSTM and LSTARGARCHLSTM models, the sample is split between training, validation and out-of-sample elements in chronological order with respective proportions of 80%, 10% and 10%
When the models are appraised in terms of the RMSE criterion, our proposed method has the highest forecasting power followed by the LSTARGARCH method
Summary
The petroleum market is currently going through one of the most volatile times in its history. Nowadays, the coronavirus disease 2019 (COVID-19) and the conflict between Russia and Saudi Arabia. Uncertainty and volatility in oil prices due to COVID-19 and the conflict between Russia and Saudi Arabia have impacted the investors’ decisions for portfolio allocation and manufacturers’ decisions for industrial production and economy. While the COVID-19 pandemic has led to worldwide recession, it has resulted in a drop in demand for oil. The petroleum market has endured the pressure of an increase in supply alongside a decrease in demand. In this respect, the differences in opinion between
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