A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices

Arash Sioofy Khoojine, Mahboubeh Shadabfar () and Yousef Edrisi Tabriz
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Arash Sioofy Khoojine: Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China
Mahboubeh Shadabfar: School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
Yousef Edrisi Tabriz: Department of Mathematics, Payame Noor University, Tehran 19395-3697, Iran

Mathematics, 2022, vol. 10, issue 17, 1-20

Abstract: The global financial markets are greatly affected by crude oil price movements, indicating the necessity of forecasting their fluctuation and volatility. Crude oil prices, however, are a complex and fundamental macroeconomic variable to estimate due to their nonlinearity, nonstationary, and volatility. The state-of-the-art research in this field demonstrates that conventional methods are incapable of addressing the nonlinear trend of price changes. Additionally, many parameters are involved in this problem, which adds to the complexity of such a prediction. To overcome these obstacles, a Mutual Information-Based Network Autoregressive (MINAR) model is developed to forecast the West Texas Intermediate (WTI) close crude oil price. To this end, open, high, low, and close (OHLC) prices of crude oil are collected from 1 January 2020 to 20 July 2022. Afterwards, the Mutual Information-based distance is utilized to establish the network of OHLC prices. The MINAR model provides a basis to consider the joint effects of the OHLC network interactions, the autoregressive impact, and the independent noise and establishes an intelligent tool to estimate the future fluctuations in a complex, multivariate, and noisy environment. To measure the accuracy and performance of the model, three validation measures, namely, RMSE, MAPE, and UMBRAE, are applied. The results demonstrate that the proposed MINAR model outperforms the benchmark ARIMA model.

Keywords: crude oil price; west texas intermediate; network autoregressive model; forecasting; mutual information; correlation matrix; multivariate time series (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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