Soybean Oil Prices Research Articles

ANALYZING THE IMPACT OF INDONESIAN SUSTAINABLE PALM OIL (ISPO) CERTIFICATION AND THE FACTORS THAT INFLUENCE CRUDE PALM OIL (CPO) EXPORTS VOLUME

This study examines the impact of Indonesian Sustainable Palm Oil (ISPO) certification on Indonesian Crude Palm Oil exports and other factors, such as commodity production and prices, exchange rates, and production levels. The data covers total CPO volume (HS codes 15111000 and 15119000) from January 2006 to December 2022, focusing on ISPO certification for large State-owned and Private Enterprises in palm oil cultivation and processing. The analysis employs the Difference-in-Differences (DID) method and multiple linear regression (MLR) to identify the linear relationship between the dependent and independent variables. The DID analysis revealed that Indonesian CPO production and global CPO prices significantly affected CPO export volumes, while soybean oil prices and effective exchange rates were not statistically significant in the regression model. The results provide valuable insights for policymakers and industry stakeholders in developing strategies that support sustainable growth, enhance competitiveness, and maintain Indonesia's leading position in the global palm oil market. Policymakers should focus on encouraging broader adoption of ISPO among palm oil producers through incentives, training, and technical support, especially for smaller producers.

Soybean overweight shock (SOS): The impact of trade liberalization in China on overweight prevalence

This study investigates the overweight effect of the soybean import shock (referred to as the soybean overweight shock or “SOS”) on China, the largest importer of soybeans worldwide. By using China's soybean import liberalization in 1996 as a natural experiment, which involved the removal of soybean tariff rate quota (TRQ) policy and a reduction in soybean import tariffs from 114% to 3%, we found significant SOS effects. The soybean import shock can account for 10.76% of the observed increase in overweight probability between 1989 and 2006. Mechanism analysis finds that the exposure to the soybean import shock leads to a significant decrease in the market price of soybean oil and an increase in soybean oil consumption, it also increases the likelihood of being overweight through dining out, eating food prepared outside, and eating fried foods. Additionally, we discovered stronger SOS effects in urban residents, females and individuals with relatively lower family income.

Towards food security: Exploring the spatio-temporal dynamics of soybean in India

India is the world’s largest edible oil importer, and soybean oil accounts for a major portion of those imports, with implications for the Indian economy. Despite being the 4th largest globally in terms of harvested soybean area and 5th largest in terms of production, India is still heavily dependent on imports to meet the vegetable oil requirement for its population. It is therefore imperative to understand the dynamics and trends in India’s soybean production to help the country achieve self-sufficiency in edible oils. This study provides the first spatially explicit analysis of soybean in India, using long-term spatial and temporal statistics at national and subnational levels, using spatial and temporal statistical analysis models to examine the historical trends and its future prospects. Our analysis details the overall soybean expansion across the country and the increase in production but we also note that the annual growth rate has declined in each consecutive decade even though the area continues to expand. The average national yield has been stagnant at around 1 T/Ha but for some of the low-producing districts, a higher yield of more than 3 T/ha is reported. For most major producing districts, soybean yields are below 1.5 T/Ha. The state of Madhya Pradesh which was the major soybean producer is now matched by the state of Maharashtra in terms of production, however, Madhya Pradesh still has the largest area under soybean. We analyzed soybean hotspot expansion in India and found that the mean center of the soybean area and production has shifted approximately 93 km towards the south and 24 km to the west as the crop is rapidly being adopted in the southern and western parts of India expanding the hotspot in these parts. District-level analysis showed that the total number of districts constituting hotspots of soybean cultivation in India has increased from 29 to 42 in three decades. Furthermore, analysis of soybean oil and meal consumption with respect to the national population, import, export, domestic production, GDP per capita, and price of soybean oil and meal suggests that soybean oil and meal are highly correlated with GDP per capita and population, indicating that consumption of soybean oil and meal is likely to increase as GDP per capita increases, and future demand is expected to rise with the anticipated growth in the Indian population. Increased soybean production can play a significant role in increasing national food security for India and reducing dependence on foreign oil imports and also help the economy with soy meal exports. Understanding the spatiotemporal variability in area and yield will help target interventions to increase production. Given the overall low yields with high variability in production, particularly in recent years primarily due to successive extreme rains and droughts in major producing districts and the overall need to increase production to meet the country’s demand, there is a pressing need for government policies and research aimed at narrowing the yield gap and developing soybean varieties that are more productive and resilient to climate change.

Industrial production of green diesel in Brazil: Process simulation and economic perspectives

The purpose of this work is to apply process simulation to infer the technical and economic bottlenecks of the industrial production of green diesel, taking as a context the Brazilian market at the end of 2021/beginning of 2022. In the considered economic scenario, a stand-alone green diesel production process does not seem economically viable in Brazil, as the fuel would need to cost 30 % more than the European market price and twice the conventional diesel price (US$ 2.08/L) to compensate for the investment made. However, significant price reductions - 10 %–15 % in the considered scenarios (to US$ 1.76–1.87/L) - can be achieved with capital and energy savings due to the availability of a conventional refinery hydrotreatment unit, and eventual parity with market prices can be achieved with soybean oil price reductions (of 22–40 %, completely possible in the Brazilian market). This analysis shows that green diesel may be absolutely possible depending on the economic context and that there is room for its gradual incorporation in petroleum diesel. This is the first research article to demonstrate it though process simulation and to economically compare the investment on a stand-alone process with a refinery retrofit.

Quantile coherency of futures prices in palm and soybean oil markets

The objective of the present work is to investigate the contemporaneous price co-movement in the futures markets of soybean and palm oil. This is pursued using quantile coherency (a statistical tool that allows for both frequency- and quantile-dependent linkages between stochastic processes) and daily futures prices from 2015 to 2023. The empirical findings suggest: (a) The co-movement between palm and soybean oil prices is not very high and, at the same time, it is asymmetric; prices in the two markets are more likely to crash than to boom together. (b) The intensity of co-movement tends to increase monotonically with the time-scale considered. However, the bulk of the adjustments to shocks tend to be completed within 1 month; the differences between coherency estimates in the medium- and in the long-run are rather small. (c) Price co-movement appears to be driven by both pure (short-run) contagion as well as by fundamental-based (long-run) contagion.

Analisis Faktor yang Mempengaruhi Ekspor Manufaktur Komoditi Minyak Kelapa Sawit Indonesia ke India

The influence of exchange rate variabels, international prices, palm oil production, soybean oil prices and India’s GDP per capita on exports of Indonesian palm oil to India is explained in this study. The technique used by the Error Correction Model (ECM) period 1990-2020 for time series data. The observation results of show that international price variable and soybean oil price have a significant for total exports of Indonesian CPO to India in the short term, but the exchange rate, palm oil production and GDP per capita of India has no significant effect. Then the exchange rate variable have a significant, while international price variables, CPO production, soybean oil prices and India's GDP per capita has no significant effect in the long term.

Estimating Total Factor Productivity and Its Components: Evidence from Major Manufacturing Industries of Pakistan

Manufacturing sector of Pakistan accounts for 19.1 percent of GDP and is the second largest sector of the economy. It grew by 8.4 percent during 2007 as against 10 percent last year. In the manufacturing sector, large scale manufacturing (LSM), plays a vital role and accounts for approximately 70 percent of overall manufacturing [Economic Survey of Pakistan (2006-07)]. During 2006-07 relatively slower pace of expansion exhibits signs of moderation on accounts of higher capacity utilisation, difficulties in the textile sector and lower than expected scale of operations of oil refineries. A number of other factors have also contributed to the low pace of expansion in manufacturing including zero percent growth in raw cotton production which is a critical input for the textile industry, vegetable ghee and cooking oil which comprise about 5.5 percent of the LSM sector, showed uninspiring performance due to unparalleled rise in international palm and soybean oil prices. The performance of the automobile sector has been far less impressive this year as compared to previous five years due to a fall in domestic demand for cars on account of increasing auto financing rates. The higher imports of used cars in the beginning of fiscal year 2006-07 also affected the performance of domestic auto mobile sector.

Palm Oil Import Demand in North America Countries

Palm oil is one of the agricultural commodities grown in world trade. North America is one of the regions with high import demand, so that the region can be observed as an export destination. This study aimed to examine the palm import demand in three selected North American Countries (United States of America (USA), Canada and Mexico) and its determinants. Autoregressive Distributive Lags (ARDL) was applied in this study between 1990 and 2017. The results indicate that the demand for palm oil in North American countries has increased. However, the import growth and shares have fluctuated relatively. GDP, GDP Indonesia, GDP Malaysia world palm oil prices, soybean oil prices, and biodiesel production significantly affect palm oil imports in the USA in the long and short-run. The GDP and GDP of Indonesia are the factors that influence palm oil import in Canada and Mexico in the long run. However, we found the impact of GDP Malaysia and GDP Indonesia in Canada in the short-run. Moreover, Indonesia's GDP and GDP significantly influenced palm oil import in Mexico. This research implies that oil palm exporting countries need to consider these factors, especially GDP, before expanding their market to North American countries as one of the biggest palm oil markets in the global world. Keywords: business analytics, biofuel, gdp, import, palm oil, price, north america

Daya Saing Ekspor Crude Palm Oil dan Refined Palm Oil Asal Indonesia dan Malaysia di Pasar India

This research examines: 1) to what extent is the competitiveness of Indonesian palm oil, especially CPO and RPO in the Indian market compared to similar products from Malaysia; and 2) what factors determine the competitiveness of Indonesian CPO and RPO exports in the Indian market. The research method uses the RCA and RSCA indices as well as the regression function to determine the dominant factors that affect the competitiveness of Indonesian CPO and RPO exports in the Indian market with data ranging from 1995 to 2021. The competitiveness of Indonesian CPO exports in the Indian market shows a more stable trend in the period 1996 to 2021 and the competitiveness of Malaysian CPO in that period has decreased slowly. The competitiveness of Indonesian and Malaysian RPO exports in the Indian market decreased in the Indian market from 1996 to 2013, then there was a strengthening of competitiveness from 2014 to 2019. The competitiveness of Indonesian CPO exports to the Indian market is influenced by the volume of Malaysian CPO exports to India, the Indonesian CPO export tax, the CPO import tax in India, the real exchange rate of the Rupiah against the US Dollar, and the real price of CPO on the world market. Furthermore, the competitiveness of Indonesian RPO exports in the Indian market is influenced by the volume of Malaysian RPO exports to India, the real exchange rate of the Rupiah against the US Dollar, the export price of RPO, the price of soybean oil on the world market, and the RPO import tax in India.

Canola and soybean oil price forecasts via neural networks

Forecasts of commodity prices are vital issues to market participants and policy-makers. Those of cooking section oil are of no exception, considering its importance as one of main food resources. In the present study, we assess the forecast problem using weekly wholesale price indices of canola and soybean oil in China during January 1, 2010–January 3, 2020, by employing the non-linear auto-regressive neural network as the forecast tool. We evaluate forecast performance of different model settings over algorithms, delays, hidden neurons, and data splitting ratios in arriving at the final models for the two commodities, which are relatively simple and lead to accurate and stable results. Particularly, the model for the price index of canola oil generates relative root mean square errors of 2.66, 1.46, and 2.17% for training, validation, and testing, respectively, and the model for the price index of soybean oil generates relative root mean square errors of 2.33, 1.96, and 1.98% for training, validation, and testing, respectively. Through the analysis, we show usefulness of the neural network technique for commodity price forecasts. Our results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Export Demand of Palm Oil in Malaysia: Analysis using ARDL Approach

The growth of the palm oil industry in Malaysia has established a demand for exports among trading partners worldwide. As a result, Malaysia has gained a reputation as the world's largest producer and exporter of palm oil. Crude palm oil production in 1980 was 2.50 million tonnes; however, by 2019 it had increased to 19.86 million tonnes due to the high export demand for oil palm products. This study studies the export demand for palm oil in Malaysia from 1991 to 2019 by adopting autoregressive distributed lag (ARDL) for cointegration to analyze the export demand determinant, weighted average trading partner income, Malaysian export price, competitor export price, world export price of palm oil, world export price of soybean oil, and real effective exchange rate. The main findings reveal that the factors that determine Malaysian export demands for palm oil are palm oil price changes and trading partner income. In addition, the study also finds that European restrictions on unsustainable palm oil consumption hurt Malaysia’s export demand for palm oil.

World Vegetable Soil Competition in 1960-2019

This study aims to examine the balance and competition of world vegetable oils as a policy formulation to improve palm oil competitiveness in the balance that is partial to the welfare of palm oil farmers in Indonesia.The study used the Vector Error Correction Model (VECM) to analyze short- and long-term balance relationships with the Johansion Cointegration Test. It then analyzed the relationship of bivariate causality with the Pairwise Granger Causality Tests approach. It further analyzes shocks on the values of independent variables that are responded by dependent variables using Impulse Response and Variance Decomposition. The study used monthly data from1960-2019 sourced from the Food and Agriculture Organization (FAO) and the World Trade Organization (WTO). The results of the study proved that the balance in the short-term variable palm oil prices in one and two previous periods was negative to the price of palm oil now. Furthermore, the same findings on soybean oil prices in the previous one and two years negatively affect the price of soybean oil today. Furthermore, the price of coconut oil in the previous two periods also negatively affects the price of coconut oil now. However, the price of coconut oil variable in the previous period had a positive and significant effect on the price of palm oil. While in the long run, the variable price of soybean oil negatively affects the price of palm oil, then the price of coconut oil has a positive effect on palm oil. The results of bivariate causality tests prove that the variable relationship of palm oil prices has a two-way causality with soybean oil prices, then the price of coconut oil (PCO) has a one-way relationship with the price of palm oil (PPO). This means that changes in price of palm oil (PPO) do not affect changes in price of palm oil (PPO), while changes in price of palm oil (PPO) affect changes in price of coconut oil (PCO). Furthermore, the two-way causality relationship between price of coconut oil (PCO) and price of soybean oil (PSO), meaning that changes in price of coconut oil (PCO) will affect price of soybean oil (PSO) and vice versa. Based on the results of impulse response function and variance decomposition explained that the shock of palm oil prices responded to changes in soybean oil prices and coconut oil prices.

Asymmetric Multifractal Cross-Correlations Between Economic Policy Uncertainty and Agricultural Futures Prices

This paper investigates the fluctuation characteristics and asymmetry of cross-correlations between economic policy uncertainty (EPU) and agricultural futures prices in China and the US by using the multifractal detrended cross-correlation analysis (MF-X-DFA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). We find that the multifractal cross-correlations exist between EPU and agricultural futures prices, and the cross-correlations are anti-persistent and asymmetric. The anti-persistent cross-correlations in China are all stronger than those in the US. The multifractal degree of cross-correlation between EPU and soybean futures price is lower in China than in the US, while the multifractal degree of cross-correlation between EPU and soybean meal, soybean oil or corn futures price is higher in China than in the US. Moreover, China’s soybean futures price is more susceptible to the upward and downward trends in China’s EPU, while the US soybean meal, soybean oil, and corn futures prices are more susceptible to them in the US EPU.

DETERMINASI PERMINTAAN CPO PADA 5 NEGARA TUJUAN EKSPOR INDONESIA

This research aims to determine the impact of the Renewable Energy Directive (RED) Policy, Import Duty Rates, and other factors on Indonesian CPO Demand in the five largest export destination countries for Indonesian CPO. Panel data regression with the Random Effect Model (REM) as the relevant model and comparative advantage analysis using RCA were used in this study (Revealed Comparative Advantage). For the period2005-2019, data from the five largest CPO export destination countries for Indonesia were used, including India, China, Netherlands, Italy, and Pakistan. Domestic CPO Production in Indonesia, International CPO Prices, International Soybean Oil Prices, and India's Dummy Import Duty Rates all significantly impact demand for Indonesian Crude Palm Oil, according to the findings. Meanwhile, the rupiah exchange rate against the US dollar and the Dummy Renewable Energy Directive does not affect demand for Crude Palm Oil . Indonesia's crude palm oil (CPO) has a more significant competitive advantage than the rest of the world's CPO producers. The existence of an RCA value more excellent than one indicates that Indonesian CPO commodities have a more significant share of the global CPO commodity export market.

Identifying possible misspecification in South African soybean oil futures contracts

ABSTRACT With the inclusion of a locally traded soybean oil futures contract, that is dual-listed and cash-settled of the Chicago Board of Trade futures contract, the South African Futures Exchange (SAFEX) aimed to provide local soybean crushing plants, the opportunity for managing their exposure toward the variation in soybean oil prices using effective hedging strategies. Which is only viable assuming adequate liquidity, that is currently lacking in these futures contracts. The soybean oil contract used for hedging local price exposure should also reflect local import parity and/or be correlated to local price movements. Therefore, with most soybean oil usually being imported from Argentina, one would expect SAFEX soybean oil futures contracts to reflect the cost of imported soybean oil from Argentina. Hence, the research study used the Engle–Granger (1987) cointegration approach, alongside a range of diagnostic tests to determine whether SAFEX soybean oil futures contracts, that is dual-listed and cash-settled of CBOT settlement values is a misspecification and whether or not SAFEX soybean oil futures contracts should rather be based on the Argentina free-on-board soybean oil prices which is a much better representation of South Africa’s import parity and local industry prices.

Soybean and Soybean Oil Price Forecasting through the Nonlinear Autoregressive Neural Network (NARNN) and NARNN with Exogenous Inputs (NARNN–X)

• We develop nonlinear autoregressive neural network (with exogenous inputs) models for daily agricultural commodity price forecasting. • We explore the forecasting issue for soybeans and soybean oil over a period of greater than fifty years. • We construct simple, accurate, and stable models for the two agricultural commodities of economic significance. • The models are useful as technical forecasting tools and for policy analysis. Price forecasting is a key concern for market participants in the agriculture sector. This study explores usefulness of the nonlinear autoregressive neural network (NARNN) and NARNN with exogenous inputs (NARNN–X) for forecasting issues in data sets of daily prices over periods of greater than fifty years for soybeans and soybean oil. The exogenous input in the NARNN–X for prices of soybeans (soybean oil) are prices of soybean oil (soybeans). Through investigating various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, models resulting in accurate and stable performance for these two commodities are arrived at. The overall relative root mean square errors based on the chosen NARNNs (NARNN–Xs) are 1.701% (1.695%) and 1.777% (1.775%) for soybeans and soybean oil, respectively. Usefulness of the machine learning approach for price forecasting issues of the two commodities is demonstrated, as well as potential usefulness of prices of closely related commodities for providing predictive content. Results here could be used on a standalone basis as technical forecasts or combined with fundamental forecasts for forming perspectives of price trends and conducting policy analysis. The empirical framework here should not be difficult to implement, which is an important consideration to many decision makers, and has potential to be generalized for forecasting prices of other commodities.

External and Internal Shocks and the Movement of Palm Oil Price: SVAR Evidence from Malaysia

Movements in palm oil price give important signals to various stakeholders of the palm oil industry in Malaysia. Thus, understanding external and internal factors that may affect the palm oil price is vital to the industry players for sustainability of their activities. This study investigates relative importance of external and internal shocks on the movement of palm oil price in Malaysia. Employing a structural vector autoregressive (SVAR) model on quarterly data from 1990 to 2019, the findings reveal that external shocks are more dominant in affecting the palm oil price. Shocks to the crude oil price, the prices of substitution goods (soybeans oil, rapeseed oil, and sunflower oil), the world palm oil price, and foreign income significantly affect the palm oil price in the short and medium run. The results also indicate that a shock to soybean oil price has a more profound effect on the palm oil price than a shock to rapeseed oil or sunflower oil prices, respectively. Likewise, shocks to incomes from India as well as from Netherlands create greater impacts on the palm oil price than a shock to income from the other trading partners, respectively. The study has shown the importance of external factors in affecting the palm oil industry.

Structural Analysis of the Prices of Selected Biodiesel Feedstocks

Purpose – Biodiesel, a plant-based source, is found in oil crops such as. coconut, palm, and soybean. This paper examined the behavior and price relationships of three selected biodiesel feedstocks: coconut oil, palm oil, and soybean oil. Design/Methodology/Approach – This study used monthly data for coconut oil, palm oil, and soybean oil prices from 2006 to 2020. Exploratory data analysis was implemented with time plots constructed and some descriptive statistics computed. The study performed structural analysis, namely, variance decomposition and impulse response analysis using the VAR (vector autoregressive) model to investigate the price relationships among the three biodiesel feedstocks. Findings – It was found that coconut oil, palm oil, and soybean oil prices generally did not affect one another despite being close substitutes. This implies that these commodities do not compete with each other in terms of prices. Research Implications – The findings can help understand price behavior given that these feedstocks are considered to be close substitutes and alternative sources to fossil fuel. Knowing the relationship between prices can contribute to policy development, which is parallel to the sustainable development goal (SDG) 7 that seeks affordable and clean energy sources. Lastly, this study initiates further research involving the biofuel industry worldwide since this is only focused on a few biodiesel feedstocks.

Vector error correction model relationship between three vegetable oil products

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

Price Integration Analysis of Crude Oil and Vegetable Oils

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.