Commodity Futures Market Prices: Decomposition Approach

Abstract

Financial investments on commodity markets have attracted many investigations due to its importance to the global economy, and worldwide trade as a whole. The radical price changes in commodity market prices, especially agricultural, energy and industrial metal products have significant consequences on consumers and producers of economic activities. It is very crucial to accurately estimate and predict volatility in commodity futures market prices, since continuous price fluctuations have dire consequences for investors, portfolio managers, dealers and policymakers in taking prudent and sustainable decisions. Commodity price component determination and forecasting are challenging due to remarkable price volatility, uncertainty, and complexity in the futures market. As a result, commodity futures price series is nonlinear and nonstationary. Various studies are reported in the literature, in an attempt to develop models to study the persistent changes in the commodity futures price series, but these models have failed to account for the inherent complexity in the commodity futures price series. This study aims to use decomposition techniques, combined with back-propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models to address difficulties in studying commodity futures market prices. As said earlier, this study utilized the decomposition methods, Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze the daily real price series of three commodity futures market prices of: corn from agricultural products, crude oil from energy, and gold from industrial metal, using the data from 4th May 2016 to 30th April 2021. In the first part of the study, we explored the descriptive and statistical properties of the data. It was found that the three commodities market futures prices series were nonstationary and nonlinear. Subsequently, we performed an EMD-Granger causality test to establish the spillover effects among the three commodities’ markets. It was revealed that there exists a strong mutual relationship among the three commodity markets price series, which implies that the price movement of one market can be used to explain the price fluctuations of the other markets. In the second part, the EMD and VMD methods were applied to decompose the daily data of each commodity price from different periods and frequencies to their respective individual intrinsic mode functions. First, we used the Hierarchical Clustering Method and Euclidean Distance Approach to classify the IMFs, residue, and modes into high-frequency, low-frequency, and trend. Next, applying statistical measures, particularly, the Pearson product-moment correlation coefficient, Kendall rank correlation, and Spearman rank correlation coefficient, we observed that the trend and low-frequency parts of the market prices are the main drivers of commodity futures markets prices’ fluctuations and that special events caused the low frequency. In essence, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary supply-demand disequilibrium. The third part compared the EMD and the VMD- based models using three forecasting performance evaluation criteria and statistical measures, such as, mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MAPE) to compare the capabilities of the suggested models. We also introduced Diebold Mariano (DM) test in selecting the optimal models for each commodity, since MAE, RMSE and MAPE have some shortcomings. The combined models outperformed the individual back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models in forecasting the series of corn and crude oil’s futures prices. At the same time, BPNN emerged as the optimal model for predicting gold futures prices’ series. In addition, variational mode decomposition emerged as the ideal data pre-treatment method and contributed to enhancing the predicting ability of the BPNN and the ARIMA models. The empirical results showed that models combined with decomposition methods predict commodity futures prices accurately and can easily capture the volatility in commodity futures prices. By utilizing the decomposition-based models in studying commodity market prices, the study filled the following gap in the existing literature as follows: the pre-treatment effect of the EMD and VMD can be compared horizontally, in decomposing commodity market price series and studying the underlying components that cause the above mentioned commodity markets price fluctuations is a novel approach in studying commodity market prices. In addition, utilizing Hierarchical Clustering and Euclidean Distance Approaches, the IMFs, residue and modes were classified into their distinctive frequencies, namely, high-frequency, low-frequency, and trend units. The effect of these frequencies and trends on commodity market price fluctuation is the first of its kind in the literature. Furthermore, applying statistical measures such as Pearson product-moment correlation coefficient, Kendall rank correlation, and Spearman rank correlation coefficient to evaluate the contribution of the IMFs, residue, and modes to the net variance of the volatility of crude oil, corn, and gold markets price fluctuations, is an innovative approach to studying financial times series. The EMD-Causality technique proposed to study the causal relationship of corn, crude oil, and gold futures prices movement, is novel in the financial market. This new approach to study price movement of commodity markets, will provide a vital information about one commodity market to explain the other commodity market price fluctuations in various markets. Also, Decomposition of financial data before forecasting have high forecasting precision accuracy in commodity futures price prediction. Additionally, using decomposition techniques in agriculture, energy, and industrial metal commodities futures markets, effectively, minimizes the prediction complexity. Furthermore, using econometric and machine learner models incorporated with decomposition methods can capture the price series information up to acceptable degrees. Finally, decomposition-based predicting techniques can effectively raise the predicting performance capability of BPNN and ARIMA models and reduce errors, thus, the proposed novel combination method can statistically improve forecast accuracy. This study, therefore, may assist in arresting the agricultural, energy, and industrial commodities markets trends and estimate volatility risk factors accurately, consequently serving as a guide for investors, governments policymakers and related sectors such as agriculture, energy, and metal industry to take prudent and sustainable planning and investment decisions. The suggested decomposition strategy, particularly VMD-based is robust in analyzing the determinants, modeling, and forecasting commodity futures market prices fluctuations, thereby, improving forecasting precision accuracy. Remarkably, in using the decomposition approach in estimating compositions of commodity prices data series separately, different predicting strategies can be explored. For instance, based on the features of decomposed IMFs or modes, a suitable predicting technique can be considered to forecast each IMF or mode; for example, the residue can be estimated by utilizing a polynomial function, while Fourier transform can be considered in predicting low-frequency IMFs or modes, hence, it is recommended that researchers, institutions, investors, and policymakers interested in studying commodity price movements should consider using this novel technique to achieve better results. It is further suggested that the decomposition approach could be utilized in other fields of study to prove the approach’s generality. Finally, further study can extend the proposed methodology by considering other decompositions techniques rather than just EMD and VMD and evaluate their robustness in studying financial markets, as EMD approach has the problem of mode mixing and endpoint effects. Eventually, we propose that a new model or consolidated predicting technique should be investigated to cater for special events’ influences on commodity market prices since no one can predict the time and the place they will occur.