World Oil And Agricultural Commodity Prices Economics Essay

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Natanelov et.al (2011) have investigate is there co-movement of agricultural commodities futures prices and crude oil. This study focused on price movement between crude oil futures and series of agricultural commodities and gold futures. A comparative framework is applied to identify changes in relationships through time and various co-integration methodologies and causality test are employed. The data used in the study comprises monthly futures prices of crude oil, cocoa, coffee, corn, soybeans, soybean oil, wheat, rice, sugar, and gold starting from July 1989 until February 2010. The Augment Dickey-Fuller (ADF) test and the Philips-Perron (PP) test are used to determine whether the series are stationary. Moreover, the Johansen co-integration, causality, causality from Vector Error Correction Model (ECM) and Threshold co-integration has been used to determine the relationship between crude oil futures with each agricultural commodities futures prices. In general, they found that mature and well established commodity futures markets exhibit co-movement with crude oil in the long run.

Oil price, agricultural commodity prices, and the dollar: A panel co-integration and causality analysis

Nazalioglu and Soytas (2011) have studied the dynamic relationship between world oil prices and 24 world agricultural commodity prices accounting for changes in the relative strength of US dollar in a panel setting. The method such as panel unit root, panel co-integration and Ganger causality has been applied in this study and a panel of 24 agricultural products based on monthly prices from January 1980 until February 2010. They found that the oil prices and the exchange rate are important factors that determine the long-run behavior of the agricultural commodity prices. Moreover, they have strong evidence to prove the impact of the oil prices on agricultural commodity prices and the positive impact of a weak dollar on agricultural commodity prices.

World oil and agricultural commodity prices: Evidence from nonlinear causality

Saban Nazlioglu (2011) has investigated the price transmission from the world oil prices to the key agricultural commodity prices (corn, soybeans, and wheat) with weekly data from 1994-2010. In this study, both linear and nonlinear Granger causality method that Toda-Yamamoto linear Granger causality test and Diks-Panchenko nonlinear Granger causality test has been applied. The empirical analysis presents 3 key findings (i) the linear Granger causality analysis supports the neutrality hypothesis which means that the oil and agricultural commodity prices do not influence each other, (ii) the nonlinear Granger causality test shows that there are nonlinear causal linkages between the oil and the agricultural commodity prices, and lastly (iii) the nonlinear causality from the oil prices to corn and soybeans prices seems to be strict. The findings from this study show the key points for better understanding of agricultural commodity prices and some policy implications for government, farmers, and global investors.

The impact of petroleum prices on vegetable oil prices:Evidence from co-integration tests

Hameed and Arshad (2009) have investigated the long term relationship between petroleum prices and vegetable oil prices from January until March 2008. The Engle-Granger two-stage estimation procedure is applied to test the co-integration among the petroleum, palm, soybean, sunflower and rapeseed oils prices. Co-integration Tests was conducted using Johansen’s maximum likelihood approach to test the relationship between petroleum oil price and each of the vegetable oils. In the short run, both tests reject the absence of a co-integrating relationship between the crude oil and vegetable oil prices series at the level of 0.05 whereas the prices tended to move towards this equilibrium relationships in the long run.In this study, they found that there exist long run equilibrium and unidirectional causality from petroleum price to each selected vegetable oil prices and the growing price of petroleum is significance in the vegetable oils complex.

Examining The Long Term Relationship Between Crude Oil And Food Commodity Prices: Co-integration And Causality

Ghaith and Awad (2011) have investigate the possible long-term relationship between the prices of crude oil and food commodities represented by maize, wheat, sorghum, soybean, barley, linseed oil, soybeal oil and palm oil. The period chosen for this study started from January 1980 until December 2009. Method of time series econometric techniques have been applied in this test is Unit root tests, Co-integration, and Granger causality. The results of this study reveal that there is a strong evidence of long-term relationship between crude oil and the food commodities prices. A traditional Granger Causality is used to check whether causality exists between two product prices. The outcome suggests that there is a long-run relationship between petroleum and food commodities under examination in this study at the 0.05 level of significance and better, except for rice at 0.1. Error-Correction Model is presented in order to check the model. The result of ECM, the error correction term for all variables holds the correct sign which show a unidirectional causality between the prices crude oil and some of the food commodities under examination.

A Time Series Analysis of the Relationship Between Total Area Planted, Palm Oil Price and Production of Malaysian Palm Oil

Asari et al (2011) have analyzed the relationship between total area planted and palm oil price with production of palm oil. Time series analysis method such as Johansen co-integration, error correction model and Granger causality test were applied to estimate those relationships. This studied developed a simple theoretical model that integrates the factor that influence the production of palm oil in Malaysia by 37 observations of palm oil production, total area planted and palm oil price from 1972 to 2008. The findings show that both researchers achieved both research objectives and the production of palm oil in Malaysia can influence its price level. Moreover, the results show that there is no causality relationship between total area planted and the production of palm oil in Malaysia. In the short run, the total area planted and palm oil production does not influence each other. By the way, there is negative relationship between the production of palm oil with the total area planted and palm oil price. They believe that there are other factors that may affect the performance of the palm oil production in Malaysia.

Analysis Of Price Trends Of Crude Oil, Agricultural Commodities And Policy Choices Of Biofuels In Developing Countries

Guo et al (2011) have analyzed the price trends of crude oil and agricultural commodities prices in developing countries. This study was carried out from July 2001 until June 2011. The unit root test method Augmented Dickey Fuller (ADF) Test was used to testing series of the crude oil prices on the prices of soybeans, corn and wheat. The results show that ADF test statistics are all less than the test critical values, and the first-order differential value of the variables is stable. The results show that the time series are stable. Hence, the causality relationship between grain prices and crude oil price can be continued to test. Next, the Granger causality test has been applied to test the relationship between crude oil prices on selected agricultural commodities prices. The findings show that there are close relationship between them, and the fluctuations of agricultural commodities prices is the results of crude oil price changes. They determined that because of the scarcity of crude oil resources, crude oil price increasing is inevitable. According to the price trends of crude oil and agricultural commodities in international market, developing countries should make their own policies of bio-fuels referring to the experience of developed countries, while basing on their resources and socio-economic conditions.

The Relationship Between Oil, Exchange Rate and Commodity Prices

Harri, Nalley and Hudson (2009) investigate the relationship between oil, exchange rate, and commodity prices. This study carried out from January 2000 to September 2008 time periods. The results for ADF unit root tests confirm the lack of stationary in levels for all series. Moreover, the presence of co-integrating relations between the crude oil with corn, soybeans, soybean oil, cotton and wheat could be determined. Besides that, AIC and SBC criteria were used to first determine the lag length for the pair wise relations. The results show that the lag length is four for corn, soybeans, and soybean oil and two for cotton and wheat. This result indicates that there were longer dynamic relations between crude oil and corn, soybean oil than between crude oil and cotton and wheat. Subsequently, wheat is excluded from further analysis. Next, the presence of co-integrating relations between crude oil, corn and exchange rates was tested. Johansen co-integrating tests were applied and the results show two cases, one where a constant is included in the error correction component but not in the autoregressive component of the Vector Auto Regression (VAR) model. The other case allows for a constant to be included in the autoregressive component of the VAR model but not in the error correction component. So, this first co-integrating relation was interpreted as the one between corn, crude oil and the exchange rate whereas the second co-integration relation as a relationship between the exchange rate and crude oil. The results was fail to reject the assumption of normality, homoscedasticity and no autocorrelation in the residuals for the three equations. Hence, the tests of weak exogenous show that the null hypothesis cannot be rejected at the 5% level of significance for crude oil when it is rejected for corn and the exchange rate. As a result, the presence of co-integrating relations between selected agricultural commodities such as corn, soybeans, soybean oil and cotton and crude oil and exchange rate was tested. The results suggest that all of these prices are interrelated.

Factors Affecting the Performance of Indonesia’s Crude Palm Oil Export

Sulistyanto and Akyuwen (2011) investigate the factors that affecting the performance of CPO export. This studied was carried out during the 1990-2007 periods in Indonesia. The main tool of analysis was multiple regressions with 38 years data. There are 5 variables which have significant impacts on the CPO export volume such as export financing, CPO export price, negative campaign, soybean oil and sunflower oil price. Moreover, variables which have no impacts are domestic CPO price, domestic CPO consumption, CPO production volume, and exchange rate, per capita GDP of the main importer countries, crude oil prices and deregulation policy. The export financing has positive impact on CPO export volume, while the price has negative impact. They found that the export volume is elastic to the CPO price in the world market. The rising of CPO price is determined by various factors include crude oil price and economic conditions. Until 2006, the CPO price is strongly determined by the other vegetable oil prices, especially soybean. Since 2007, the dominant factor is the demand for biodiesel and the rising of crude oil price. If compared to the other vegetable oils, the CPO is the most suitable vegetable oil to replace crude oil as the energy source.

Examining the Evolving Correspondence Between Petroleum Prices and Agricultural Commodity Prices

Campiche et al (2007) have examined the evolving correspondence between petroleum price and agricultural commodities prices. This study carried out during the 2003-2007 time periods. Co-integration test such as Johansen approach and the Engle Granger approach was also employed to analyze issues associated with non-stationary time series data, while avoiding the problem of spurious regression. The co-integration test revealed that none of the agricultural commodity price series were co-integrated with the crude oil price during the 2003-2005 time frames. However, corn and soybean prices were co-integrated with the crude oil price during the 2006-2007 time periods. The unit root test method Augmented Dickey Fuller (ADF) Test was used to testing series which indicated that all variables were non-stationary in levels and stationary in first differences. The Schwarz Information Criterion (SIC) indicated an optimal lag length of one. Tests for weak exogenous indicated that the crude oil price was weakly exogenous in both co-integrating relationships. A thorough understanding of the inter-relationship among the prices of agricultural commodities and fossil prices is essential for producers and policy makers to make decision.

Revisiting the Palm Oil Boom in Southeast Asia (The Role of Fuel versus Food Demand Drivers)

Sanders et al (2012) have examined the relationship among palm oil prices, soybean oil prices and crude oil prices. The analysis used the time series from 1980 to 2010 and employs different time series based econometric models to identify interactions among the three price series in order to shed light on the cause of the growth of palm oil demand. Two models of oil price systems have been estimated that a simple Vector Auto Regression (VAR) model treats all three prices as stationary as well as and a Vector error Correction model (VECM) that allows co-integration among the three prices. VAR and VECM find that palm oil prices do not appear to respond to short-run fluctuations in crude oil prices. Even though, the palm oil prices are a function of lagged palm oil prices and current and lagged soybean oil prices in the short run. Consequently, short-run fluctuations in crude oil prices do not appear to be a driver of the boom in the palm oil productions while short-run fluctuations in the soybean oil prices do affect palm oil markets. The VECM model also indicates a long-run equilibrium relationship among prices of palm oil, soybean oil and crude oil. Palm oil prices and crude oil prices are negatively correlated in the long run. These results show that a potentially important relationship in short and long run between palm oil markets and soybean oil markets, but this analysis does not point to the crude oil market as an important driver of palm oil boom.

An Economic Analysis of the Malaysian Palm Oil Market

Talib and Darawi (2002) described the national model of the Malaysian palm oil market and also identified the important factors that affecting the Malaysian palm oil industry from 1970 until 1999. Ordinary Least Squares (OLS) was applied to estimate the area, yield, domestic consumption, exports and imports equations. The equations were estimated with the assumption of independence among the exogenous variables and error terms with zero mean and constant variance. But, since the equations contain lagged dependent variables, OLS yields biased estimates since the residuals are auto-correlated. So, a test for the incidence of auto-correlation was used in these equations. Yet, the two stages least squares (2SLS) method is more suitable compared to OLS. The reason is some of the equations were also determined by endogenous variables. , F-statistic, t-statistic, Durbin-Watson (DW) and Durbin-h tests were used to evaluate the estimated model. The Durbin-h statistic was used to test for first-order autocorrelation when a lagged dependent variable was included as an explanatory variable in the regression. Both the OLS and 2SLS method estimates of total area equation show the values of the F-statistic, SEE and are statistically acceptable. However, the Durbin-h statistic cannot be computed due to the number in the square root formula was negative number. Therefore, Lagrange multiplier (LM) was used to test for the presence of first-order autocorrelation. The results indicate that the change in either palm oil or natural rubber price is not very important in determining the total area of oil palm in Malaysia, even in the long run. Both OLS and 2SLS method estimates of yield equation are statistically acceptable. Value of is only 38% of the variation in oil palm yields during the sample period is explained by the specified variables. LM test shows that there is no strong evidence of first-order auto-correlation. The estimation of consumption equations shows that given a 10% increase in industrial production index, the domestic consumption would only increased by 3% in the short run. The coefficient of the current price of palm oil is negative while the coefficient of current price of coconut oil follows the expected sign. On the other hand, both coefficients are not statistically significant which implies that the consumption level of palm oil does not merely depend on the levels of both prices. The estimation of exports equations show that the Durbin-h and LM test reveal no evidence of serial correlation in the results. The results of the imports equations show that the coefficient of the Malaysian industrial production is found to be positive and statistically significant at 1% level. Subsequently, the estimation results of the Malaysian palm oil market model are statistically acceptable and have identified many important factors related to area and yield of oil palm in Malaysia, as well as domestic consumption, exports, and imports of palm oil.

Price Discovery through Crude Palm Oil Futures: An Economic Evaluation

Arshad and Mohamed (1994) have investigated the forward pricing efficiency of the local crude palm oil (CPO) futures market from 1983 to 1992. The relative predictive power of futures price is compared with the various forecasts estimated from proven forecasting techniques like moving average, exponential smoothing, Box Jenkins and econometric. Traditional Efficient Market Model has been applied in this study in order to tests the hypothesis that futures prices reflecting the subjective through rational expectation of traders, are unbiased estimates of futures spot prices. Moreover, this study also compares the price performance of various months of CPO futures contract one to five months before delivery. The results shows that the shorter the future contract which indicate that the shorter is time distance between the quoting date of the future price and delivery date, the higher is the value of . The standard error increases as the time distance is further from delivery date. The Durbin Watson (DW) statistics also shows there is no serial correlation for all the months before delivery. Nevertheless, F-statistic is significant at 5% significant levels. Rausser and Carter Efficient Market Model was applied in order to find the best forecasting model which would be the one that produces the lowest, RMPSE and U-statistic. The results found that the futures price quoted on month advance provides a relatively accurate forecast of spot prices with RMSE of 109.83, RMPSE of 9.94 and U-Statistic of 0.25. Moving Average model was the alternative model which produces the forecast of spot prices with RMSE of 64.39, RMPSE of 5.79 and U-Statistics of 0.5. Box-Jenkins and the smoothing techniques was also used and the results provide less impressive forecasts with U-statistics ranging between 0.91 - 0.94. Moving average for futures price fail to provide good results as the predictive statistics are relatively large which the U-statistic are more than 1. As a result, the future price outperforms the other techniques in forecasting forward price which indicate that the nearer the maturity date its forecasting ability improves significantly.

Progress Accuracy of CPO Price Prediction: Evidence from ARMA Family and Artificial Neural Network Approach

Karia and Bujang (2011) have done a forecasting of CPO prices on daily, weekly and monthly CPO price over the period of study from January 2006 to December 2010 in Malaysia. The Box Jenkins and Neutral Network were used to forecast the CPO price. The results show that neutral network achieve lower MSE as compared to ARIMA (2,1,1). Thus, the model of ARIMA (2,1,1) is not suitable to forecast daily CPO prices which in turn, the neutral network is better in forecasting daily CPO price. On the other hand, the performance of neutral network degrades when it comes to measuring the weekly and monthly data of CPO prices. Box Jenkins show much better in forecasting performance when it comes to weekly and monthly basis. In a weekly data, the Box Jenkins produces 0.0001 of the MSE in the ARIMA (1,1,0) model which indicate smaller errors than neutral network with MSE of 0.1171. In a monthly basis, the results suggest that the ARMA (1,1) show better result as compared to ANN as 0.0192<0.0318. So, ARMA(1,1) model is more appropriate in order to forecast monthly CPO prices. Therefore, Box Jenkins only gives better prediction when the prediction deals with the low frequency of the time series data. So, it is best to conduct fuzzy logic approach in order to produce more accurate prediction when there is existence of the linear and nonlinear pattern in the time series.

Interdependencies in the energy-bioenergy-food price systems: A co-integration analysis

Ciaian and Kancs (2011) have investigated the interdependencies between the energy, bio-energy and food prices. They have developed a vertically integrated multi-input, multi-output market model with 2 channels of price transmission that a direct bio-fuel channel and an indirect input channel. The theoretical has been test by applying time-series analytical mechanisms to 9 major traded agricultural commodity prices, including wheat, rice, corn, sugar, cotton, soybeans, banana, tea, and sorghum along with one weighted average world crude oil price. The data consists of 783 weekly observations from January 1994 until December 2008. The econometric approach such as Augmented Dickey-Fuller (ADF) and Philips Perron (PP) unit root test were applied. Both ADF and PP unit root tests of first differences reject the null of a unit root for the ten prices and suggest that nine agricultural commodity and crude oil prices in all three periods are integrated of order one. Moreover, the Johansen co-integration, causality from Vector Error Correction Model (ECM) and Granger causality have been used to determine the relationship between crude oil futures with each agricultural commodities prices.As a result, the prices of crude oil and agricultural commodities are interdependent from the analysis and there is a long-run Granger causality from oil to agricultural commodity prices, but not vice versa.

Probability Distribution of Return and Volatitlity in Crude Oil Market

Tung and Hai (2008) investigate the returns and volatility of crude oil market. This study carried out from January 1, 1986 to December 31, 2007 time periods. Probability distribution techniques were applied to determine whether there are any probability distribution differences exist in returns and volatility of crude oil market. Using these techniques, potential biases which resulting from only looking at the mean, standard deviation of returns and neglecting the other parameters of the distribution can be voided. Firstly, the time series of daily returns for crude oil market was examined and the results show the maximum and minimum returns are 0.4367 and -0.4035. This may suggest the presence of sharp discontinuities in the series. In order to display the probability distribution of daily returns more clearly, probability distribution for the time series of daily returns was constructed. Histogram method by grouping the total samples into 100 intervals was adopted in order to construct the probability distribution. In order to further analyze the characteristics of the probability distributions, Gaussian function was being chosen to fit original daily returns. The results show that the average return for crude oil market is 0.0244, with a standard deviation of 0.0290. The results show that the distribution is slightly left-skewed (with a skewness of -0.0115) and lepto-kurtosis (with a kurtosis of 37.9153). Besides, the Jarque-Bera test statistic and its corresponding p-value which is significant at 0.05 showing that null hypothesis of a normal distribution are rejected. The crude oil market is an unstable and volatile market, after estimating the peak and width of the volatility of the log-normal distribution. All these findings are important to market traders and hedging strategies.

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