scholarly journals Determinants of Commodity Futures Prices: Decomposition Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Emmanuel Antwi ◽  
Emmanuel N. Gyamfi ◽  
Kwabena Kyei ◽  
Ryan Gill ◽  
Anokye M. Adam
Keyword(s):  
Correlation Coefficient ◽  
Market Price ◽  
Low Frequency ◽  
Rank Correlation ◽  
Commodity Price ◽  
Commodity Futures ◽  
Futures Prices ◽  
Special Events ◽  
Statistical Measures ◽  
Mode Decomposition

Developing models to analyze time series is a very sophisticated, time-consuming, but interesting experience for researchers. Commodity price component determination is challenging due to remarkable price volatility, uncertainty, and complexity in the futures market. This study aims to determine the components that drive the market price of commodity futures. This study utilized the decomposition methods, empirical mode decomposition (EMD), and variational mode decomposition (VMD), to analyze three commodity futures prices data: corn from agricultural products, crude oil from energy, and gold from industrial metal. We applied these techniques to decompose the daily data of each commodity price from different periods and frequencies into individual intrinsic mode functions for EMD and modes for VMD. We used the hierarchical clustering method and Euclidean distance approach to classify the IMFs 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 price are the main drivers of commodity futures markets’ price fluctuations. The low-frequencies are caused by special events. In a nutshell, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary disequilibrium of supply-demand.

scholarly journals Drivers of Stock Prices in Ghana: An Empirical Mode Decomposition Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Emmanuel. N. Gyamfi ◽  
Frederick A. A. Sarpong ◽  
Anokye M. Adam
Keyword(s):  
Empirical Mode Decomposition ◽  
Stock Prices ◽  
Low Frequency ◽  
Rank Correlation ◽  
Stock Index ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Statistical Measures ◽  
Mode Decomposition ◽  
Kendall Rank Correlation

This study utilized the empirical mode decomposition (EMD) technique and examined which group of investors based on their trading frequencies influence stock prices in Ghana. We applied this technique to a dataset of daily closing prices of GSE Financial Stock Index for the period 04/01/2011 to 28/08/2015. The daily closing prices were decomposed into six intrinsic mode functions (IMFs) and a residue. We used the hierarchical clustering method to reconstruct the IMFs into high frequency, low frequency, and trend components. Using statistical measures such as Pearson product moment correlation coefficient and the Kendall rank correlation, we found that the low frequency and trend components of stock prices are the main drivers of the GSE stock index. These low-frequency traders are the institutional investors. Therefore, stock prices on the GSE are affected by real economic growth but not short-lived market fluctuations.

Financialization of Commodity Markets

2018 ◽  
Author(s):  
Kyle J. Putnam
Keyword(s):  
Business Cycle ◽  
Futures Markets ◽  
Commodity Price ◽  
Commodity Markets ◽  
Commodity Futures ◽  
Futures Prices ◽  
Price Behavior ◽  
Risk And Return ◽  
Commodity Futures Markets ◽  
Asset Class

In the early 2000s, financial investors began pouring billions of dollars into the commodity futures markets seeking the unique investment benefits of this distinct asset class. This “financialization” process has called into question the fundamental risk and return properties of commodity futures as evidence has emerged favoring the idea that the massive increase in investor flows caused a rise in futures prices, volatility, and intra- and intermarket return correlations. However, a contrarian line of research contends that the effects of the new “speculative” capital on the futures markets are unsubstantiated and the increased participation of financial investors poses little consequence to the economics of the marketplace. This latter line of literature maintains that the investment benefits of commodity futures have not been diminished and that fundamental factors and business cycle variations can explain the observed changes in commodity price behavior.

scholarly journals Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Ruoyang Chen ◽  
Bin Pan
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures ◽  
Price Fluctuations ◽  
Mode Decomposition ◽  
Svm Model

Since the CSI 300 index futures officially began trading on April 15, 2010, analysis and predictions of the price fluctuations of Chinese stock index futures prices have become a popular area of active research. In this paper, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is used to decompose the sequences of Chinese stock index futures prices into residue terms, low-frequency terms, and high-frequency terms to reveal the fluctuation characteristics over different time scales of the sequences. Then, the CEEMD method is combined with the Particle Swarm Optimization (PSO) algorithm-based Support Vector Machine (SVM) model to forecast Chinese stock index futures prices. The empirical results show that the residue term determines the long-term trend of stock index futures prices. The low-frequency term, which represents medium-term price fluctuations, is mainly affected by policy regulations under the analysis of the Iterated Cumulative Sums of Squares (ICSS) algorithm, whereas short-term market disequilibrium, which is represented by the high-frequency term, plays an important local role in stock index futures price fluctuations. In addition, in forecasting the daily or even intraday price data of Chinese stock index futures, the combination prediction model is superior to the single SVM model, which implies that the accuracy of predicting Chinese stock index futures prices will be improved by considering fluctuation characteristics in different time scales.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

Pricing of Futures Contracts

2018 ◽  
Author(s):  
Timothy A. Krause
Keyword(s):  
Fair Value ◽  
Commodity Futures ◽  
Futures Contracts ◽  
Spot Price ◽  
Futures Prices ◽  
No Arbitrage ◽  
Spot Prices ◽  
Cost Of Carry ◽  
Contract Expiration ◽  
The Cost

This chapter examines the relation between futures prices relative to the spot price of the underlying asset. Basic futures pricing is characterized by the convergence of futures and spot prices during the delivery period just before contract expiration. However, “no arbitrage” arguments that dictate the fair value of futures contracts largely determine pricing relations before expiration. Although the cost of carry model in its various forms largely determines futures prices before expiration, the chapter presents alternative explanations. Related commodity futures complexes exhibit mean-reverting behavior, as seen in commodity spread markets and other interrelated commodities. Energy commodity futures prices can be somewhat accurately modeled as a generalized autoregressive conditional heteroskedastic (GARCH) process, although whether these models provide economically significant excess returns is uncertain.

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