scholarly journals Price Integration Analysis of Crude Oil and Vegetable Oils

HABITAT ◽  
2021 ◽  
Vol 32 (2) ◽  
pp. 82-92
Author(s):  
Resti Destiarni ◽  
Ahmad Jamil
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Vegetable Oil ◽  
Vegetable Oils ◽  
Palm Oil ◽  
Oil Prices ◽  
Oil Price ◽  
Cointegration Test ◽  
Price Data ◽  
Long Run

The importance of palm oil as Indonesia's main export commodity from the non-oil and gas sector makes a study about the price integration of crude oil and vegetable oils is conducted. The time-series data is used are monthly data from 2002:2 to 2019:4. Using the Vector Correction Model (VECM), this study aimed to analyze the price integration among Log of Crude Oil Price (LCOP), Palm Oil Price (LPOP), Soybean Oil Price (LSOP), Sun Flower Oil Price (LSFOP) and Rapeseed Oil Price (LROP). Augmented-Dickey Fuller (ADF) stationary test results show that the time series for those data are stationary at first difference. Using the Pearson Correlation test among price data indicates that there is a high positive correlation among those price data. It reveals a high degree of short-run integration among oil price data. Based on the Johansen cointegration test, the result reveals the presence of long-run relationships among determinants. Knowing presence of cointegration among the data, a bivariate cointegration test was conducted in this study. The test showed that LCOP did not have long-run relationship with vegetable oil prices. The Engel Granger Causality test revealed that generally, LPOP have influence on the movement both LCOP and other vegetable oil prices.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

scholarly journals Impact of FDI inflow, crude oil prices, and economic growth on CO2 emission in Tunisia: Symmetric and asymmetric analysis through ARDL and NARDL approach

2021 ◽  
Vol 12 (1) ◽  
pp. 1-13
Author(s):  
Tarek Ghazouani
Keyword(s):  
Economic Growth ◽  
Crude Oil ◽  
Carbon Emissions ◽  
Co2 Emission ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Fdi Inflows ◽  
Crude Oil Prices ◽  
Long Run

This study explores the symmetric and asymmetric impact of real GDP per capita, FDI inflow, and crude oil price on CO2 emission in Tunisia for the 1972–2016 period. Using the cointegration tests, namely ARDL and NARDL bound test, the results show that the variables are associated in a long run relationship. Long run estimates from both approach confirms the validity of ECK hypothesis for Tunisia. Symmetric analysis reveals that economic growth and the price of crude oil adversely affect the environment, in contrast to FDI inflows that reduce CO2 emissions in the long run. Whereas the asymmetric analysis show that increase in crude oil price harm the environment and decrease in crude oil price have positive repercussions on the environment. The causality analysis suggests that a bilateral link exists between economic growth and carbon emissions and a one-way causality ranges from FDI inflows and crude oil prices to carbon emissions. Thus, some policy recommendations have been formulated to help Tunisia reduce carbon emissions and support economic development.

scholarly journals Vector error correction model relationship between three vegetable oil products

2021 ◽  
Vol 892 (1) ◽  
pp. 012062
Author(s):  
A Rifin ◽  
D Nauly
Keyword(s):  
Soybean Oil ◽  
Vegetable Oil ◽  
Palm Oil ◽  
Oil Prices ◽  
Correction Model ◽  
Vector Error Correction ◽  
Vector Error ◽  
Long Run ◽  
Short Run ◽  
International Price

Abstract International price of palm oil fluctuated frequently. It is predicted that the international price of palm oil is affected by the other vegetable oil prices. Soybean oil, rapeseed oil and palm oil are the three most important vegetable oil in the word. These commodities compete but on the other hand the world prices are moving in the same direction. This paper analyzes the relationship of these three prices in the short-run and long-run. The method utilizes in the analysis is the vector error correction model (VECM) followed by Impulse Response and Variance Decomposition. The data used is monthly data from January 2003 until December 2020. The results indicate that in the short-run, only the lag of each vegetable oil prices affects their own price. Meanwhile, in the long-run the three prices have long-run relationship or in other words the prices are cointegrated. Using variance decomposition and impulse response shows that soybean oil price has more effect on rapeseed and palm oil prices. Therefore, it can be concluded, the fluctuation of rapeseed and palm oil prices will be affected by the price fluctuation of soybean oil price

scholarly journals Test for asymmetry on the ex-pump price of premium gasoline in Ghana, Kenya, and Colombia

2021 ◽  
Vol 4 (1) ◽  
pp. 73-89
Author(s):  
Senanu Kwasi Klutse ◽  
Gábor Dávid Kiss
Keyword(s):  
Exchange Rate ◽  
Crude Oil ◽  
Oil Prices ◽  
Oil Price ◽  
Dollar Exchange Rate ◽  
Fuel Prices ◽  
Crude Oil Prices ◽  
Long Run ◽  
The Us ◽  
Us Dollar

Once again, the World has been faced with an oil price shock as a result of the SARS-CoV-2 coronavirus pandemic. This has resurrected an old debate of whether retail fuel prices adjust significantly to either increases or decreases in international crude oil prices. With many countries moving towards the deregulation of their petroleum sub-sector, the impact of the US dollar exchange rate on retail fuel prices cannot be overlooked. This study investigates the rate at which positive and negative changes in international Brent crude oil prices and the US dollar exchange rate affected the increases or decreases in the ex-pump price of premium gasoline between February 2012 and December 2019. Using a non-linear auto-regressive distributed lag model, the exchange rate was found to play a significant role in fluctuations in the retail price of premium gasoline in Ghana and Colombia in the long run, howev-er, the rate of adjustment between the negative and positive changes was not significant, dispelling the perception of price asymmetry. There was no significant relationship between the ex-pump price of premium gasoline and the international Brent crude oil price in Ghana and Kenya in the long run. This study recommends that the aforementioned countries prioritise the creation of ex-change rate buffers to prevent exchange rate shocks that may affect retail fuel prices.

scholarly journals Implications of Oil Price Fluctuations for Tourism Receipts: The Case of Oil Exporting Countries

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4349
Author(s):  
Siamand Hesami ◽  
Bezhan Rustamov ◽  
Husam Rjoub ◽  
Wing-Keung Wong
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
Oil Prices ◽  
Oil Price ◽  
Significant Finding ◽  
Price Fluctuations ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Oil Exports ◽  
Systematic Understanding

This study investigates the influence of oil prices on tourism income in countries that heavily relied on crude oil exports from 2000 to 2017. We found that oil prices and tourism receipts are cointegrated, revealing the existence of their long-run equilibrium relationship. Another significant finding to emerge from this study is the presence of a unidirectional Granger causality that runs from the oil prices to the tourism receipts. The results of the current study are of particular importance for policymakers who operate in oil-exporting countries. The implications provide a systematic understanding of the effect of oil price fluctuations on tourism income which can benefit investors greatly by enabling them to hedge against oil price fluctuations and plan for their tourism business and policymakers by enabling them to set policies to stabilize oil price fluctuations and plan for tourism development, correspondingly.

scholarly journals A cointegration analysis of crude palm oil price in Thailand

2018 ◽  
Vol 52 ◽  
pp. 00035
Author(s):  
Patchaya Songsiengchai ◽  
Shaufique F. Sidique ◽  
Marcel Djama ◽  
W.N.W. Azman-Saini
Keyword(s):  
Soybean Oil ◽  
Crude Oil ◽  
Error Correction ◽  
Palm Oil ◽  
Commodity Prices ◽  
Cointegration Analysis ◽  
Oil Price ◽  
Crude Palm Oil ◽  
Long Run ◽  
The World

Reliazing the pass-through effects of global commodity prices on domestic prices, this study develops a vector error correction model (VECM) to test for the determinants and direction of causality between global prices and crude palm oil (CPO) price in Thailand. Malaysian crude palm oil, world soybean oil and world crude oil prices were investigated as factors affecting the Thai CPO price. Using the Johansen cointegration test, the result unveils a presence of long-run relationship among the determinants. This long-run relationship, proposes that CPO price flows in Thailand are positively influenced by the Malaysian CPO price and the error correction term suggests that approximately 35 percent of total disequilibrium in Thai CPO price was corrected in the following month. Moreover, the findings show Granger causality from each of the Malaysian CPO price and the world soybean oil price for the Thai CPO price. Information flow regarding the price movements of the Malaysian CPO and soybean oil affect the Thai CPO price and vice-versa. Whereas, the evidence for a causal relationship that runs from the world crude oil price to the Thai CPO price is found, but not in reverse.

scholarly journals Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Crude Oil ◽  
Oil Prices ◽  
Principal Component ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Swarm Optimization ◽  
Crude Oil Prices

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

scholarly journals Investigating the Asymmetry Effects of Crude Oil Price on Renewable Energy Consumption in the United States

2021 ◽  
Author(s):  
Pritish Sahu ◽  
Sakiru Adebola Solarin ◽  
Usama Al-mulali ◽  
Ilhan Ozturk
Keyword(s):  
Renewable Energy ◽  
Crude Oil ◽  
Energy Use ◽  
Oil Prices ◽  
The United States ◽  
Oil Price ◽  
Crude Oil Price ◽  
Renewable Energy Consumption ◽  
Long Run ◽  
Short Run

Abstract The reduction in oil prices might make crude oil a cheaper alternative to renewable energy. Given this, the present paper examines the effect of fluctuation of oil prices on the use of renewable energy in the United States during the period 1970–2019. We constructed two nonlinear Autoregressive Distributed Lag (NARDL) models to examine the effect of the positive and negative oil prices shocks on the use of renewable energy in the US. The renewable energy consumption is taken as the dependent variable and GDP, Brent crude prices, population density, trade openness and price index as independent variables. The result revealed that the rise in crude oil price, GDP and population density will increase renewable energy use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease renewable energy use in the short run and its effect will eventually diminish in the long run.

scholarly journals Oil price Fluctuation, Gold Returns and Inflationary Pressure: An Empirical Analysis Using Cointegration Approach

2019 ◽  
Vol 6 (2) ◽  
pp. 71 ◽  
Author(s):  
Hanan Naser
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Oil Price ◽  
Price Fluctuation ◽  
Inflationary Pressure ◽  
Crude Oil Prices ◽  
Long Run ◽  
Short Run ◽  
The Impact ◽  
Oil Price Fluctuation

Given that oil and gold prices are the major representative for commodity market, they both play a crucial role in determining the level of consumption, industrial production and investment due to the direct effect by the changes in their prices. In addition, both oil and gold prices have inflationary pressure which has a direct impact on countries economic growth. Therefore, it is of crucial practical significance to analyze their cointrgration relationships to understand the co-movement of both prices. To do so, this paper aims to examine the impact of oil price fluctuation on gold prices taking into account the inflationary pressure in the United States (US). Using monthly data from April, 1986 to September, 2018, Johansen multivariate cointegration test procedure and vector error correction model (VECM) have been employed to examine the long-run relationship between the variables in the US. The key findings suggest that there is a significant positive long run relationship between crude oil prices, gold prices and inflation. In the short run, the impact of any changes in crude oil prices will have a delayed effect on the prices of gold, while the impact of inflation in not different from zero. In addition, both gold prices and inflation are found to have no impact on gold prices in the short run. The findings of this research are important for investors, portfolio managers, corporate houses, crude oil traders, the government and policy makers.

scholarly journals An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1239 ◽  
Author(s):  
Jiang Wu ◽  
Yu Chen ◽  
Tengfei Zhou ◽  
Taiyong Li
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Prediction Models ◽  
Oil Prices ◽  
Accurate Prediction ◽  
Oil Price ◽  
Crude Oil Price ◽  
Learning Paradigm ◽  
Crude Oil Prices ◽  
Target Values

Crude oil is one of the main energy sources and its prices have gained increasing attention due to its important role in the world economy. Accurate prediction of crude oil prices is an important issue not only for ordinary investors, but also for the whole society. To achieve the accurate prediction of nonstationary and nonlinear crude oil price time series, an adaptive hybrid ensemble learning paradigm integrating complementary ensemble empirical mode decomposition (CEEMD), autoregressive integrated moving average (ARIMA) and sparse Bayesian learning (SBL), namely CEEMD-ARIMA&SBL-SBL (CEEMD-A&S-SBL), is developed in this study. Firstly, the decomposition method CEEMD, which can reduce the end effects and mode mixing, was employed to decompose the original crude oil price time series into intrinsic mode functions (IMFs) and one residue. Then, ARIMA and SBL with combined kernels were applied to predict target values for the residue and each single IMF independently. Finally, the predicted values of the above two models for each component were adaptively selected based on the training precision, and then aggregated as the final forecasting results using SBL without kernel-tricks. Experiments were conducted on the crude oil spot prices of the West Texas Intermediate (WTI) and Brent crude oil to evaluate the performance of the proposed CEEMD-A&S-SBL. The experimental results demonstrated that, compared with some state-of-the-art prediction models, CEEMD-A&S-SBL can significantly improve the prediction accuracy of crude oil prices in terms of the root mean squared error (RMSE), the mean absolute percent error (MAPE), and the directional statistic (Dstat).

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