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India is one of the leading producers and consumers of vegetable oils in the world. The integration of ’India’s edible oils markets with international oil markets (Rotterdam market) is studied with the overall objective of establishing long-run relationship and direction of causality. Keeping in view of the quantum of arrivals, five major domestic wholesale markets and one international market each for groundnut, soybean, and sunflower were selected. Johansen’s cointegration test revealed the prevalence of long-run relationships across the markets. In the case of groundnut oil, Rotterdam market prices are influenced by only Delhi market, whereas all selected domestic markets influence the latter. The results of causality in soybean markets confirmed a unidirectional relationship between all the domestic markets with the international market except Jaipur market, which has a bidirectional relationship with the international market. Hyderabad and Vijayawada sunflower market prices influenced the international market. The suggested policy intervention is to strengthen market intelligence for farmers by establishing online market analysis and dissemination system. The development/strengthening of market infrastructure, including communication, transportation, and storage networks, is mandatory to fully integrate the markets.

Market integration occurs when prices among different locations exhibit similar patterns over an extended period.

The integration of ’India’s edible oils markets with international oil markets (Rotterdam market) is studied with the overall objective of establishing long-run relationship and direction of causality.

The development/strengthening of market infrastructure, including communication, transportation, and storage networks, is mandatory to fully integrate the markets.

The study uses time-series data on prices of groundnut, sunflower, and soybean oil in domestic and international markets. The markets selected for the study from India were Chennai, Delhi, Hyderabad, Mumbai, and Rajkot for groundnut, Hyderabad, Bengaluru, Jaipur, Mumbai, and Bhopal for soybean, Bengaluru, Chennai, Hyderabad, Nagpur, Vijaywada for sunflower and one international market for each crop. Monthly price data for selected domestic markets were collected from the website of NIC and the international prices from the Global Economic Monitor (GEM), popularly known as the pink data sheet of the World Bank for Jan- 2009 to Feb- 2020. Various statistical/time-series analytical techniques, namely ADF unit root test, Johansen’s cointegration test, and Granger causality test method, were employed to study the market integration.

The static data is the one that has a basic statistical property of constant mean and finite constant variance. The stationarity test is based on the Dickey-Fuller value statistic of β_{1} given by the following equation:

Where, ∆_{t}_{1}_{t}_{1t-1}

The test statistic is simply the _{t}_{1} is found to be negative and statistically significant, the above test can also be carried out for the first difference of the variables.

Where the null hypothesis is Ho: _{t}_{1} is found to be negative and statistically significant.

If the given data series is already stationary, i.e., if I(0) for both the series, then we say they are not co-integrated; if not, make the data stationary by differencing. Test the differenced series for stationarity by repeating the above step.

A series, which becomes stationary after first differencing, is said to be integrated into order one and expressed as I(1). Generally, a series may have been differenced ‘d’ times to become stationary in which case it is termed as I(d). A major difference between I(0) and I(d) series is that the I(0) series has a finite mean and variance, while in the I(d) series, these magnitudes do not exist. Thus, a differenced series has properties such as mean, standard deviation, and co-variance invariant with time.

If the order of integration is the same for both the series i.e., _{t}_{it}_{ij}_{it}_{ij}

The Engle-Granger two-step method was used to test for co-integration between the variables. Johansen’s Co-integration technique was used to test the long-run relationship.

This methodology is based on OLS regression. It is most suitable for bivariate settings where the choice of the dependent variable is not a question and can identify only one cointegration vector. This is a residual-based cointegration test. It seeks to determine whether the residuals of the equilibrium relationship are stationary i.e. β’_{t}_{t}_{t} stationary? This is established through the Augmented Dickey Fuller (ADF) test on residuals of the co-integrating regression results.

_{t}_{t}_{t}_{t}_{0} and β_{1}. The OLS estimates of β_{0} and β_{1} converge faster than in OLS models using static variables (Stock, 1987).

_{t}_{t}

The null and alternate hypotheses are,

H_{0}: α_{1} = 0

H_{1}: α_{1} ≠ 0

The parameter of interest in equation (_{1}. If the null hypothesis α_{1} = 0, is not rejected, it could be concluded that the residual series contains a unit root. Thus, the _{t}_{t}_{t}_{t}

_{t}_{0} + β_{1}_{t}_{t}_{t}_{t}

Where, _{i}

_{yt}_{zt}

α_{1}, α_{2′}, α_{y′}, α_{z′}, α_{11}(i), α_{12} (i), α_{21} (i) and α_{22} (i) are all parameters.

The items in parentheses are the error correction terms.

Johansen (1988) has developed a multivariate system of equations approach. The long- rum run relationship between the price series is estimated through Johansen co- integration model. The test shows whether the selected vegetable oil markets are integrated or not. This test allows for simultaneous adjustment of more than two variables. Only when two series are integrated can there be a feedback mechanism of price information and market price discovery.

Before analysing cointegration, it is necessary to check the univariate time-series data generating process to examine whether the series under study exhibit a standard stochastic dynamic process. This was analyzed by employing the ADF test, and results are presented in

Augmented Dickey-Fuller tests for selected oil markets

Johansen’s Maximum Likelihood Test (trace test) results are shown in

Johansen’s Co-integration Test for Selected Groundnut Markets

Granger causality test for different groundnut markets

A Granger causality test was also performed across the groundnut markets; the results of which are given in

Johansen’s Co-integration Test for Selected Soybean Markets

Granger causality test for different Soybean markets

Johansen’s Co-integration Test for Selected Sunflower Markets

Causal relationship among major groundnut markets under study

The, Johansen’s cointegration test has shown that even though the selected wholesale soybean markets are geographically and spatially isolated, they are well-connected in terms of prices of soybean, revealing the presence of long-run price linkages among the soybean markets.

Causal relationship among major soybean markets understudy

After finding cointegration among different soybean markets, Granger causality was also estimated between the selected pairs of soybean oil markets. The Granger causality shows the direction of price formation between two markets. The results are presented in

The integration relation between the wholesale prices of selected sunflower markets and the relationship between wholesale prices of selected sunflower markets was examined and presented in

Causal relationship among major sunflower markets under study

As a part of cointegration analysis, Granger Causality test was conducted to examine whether there was a causal relationship between the cointegrated markets as revealed by Johansen’s test. The causal relationships among major sunflower oil market prices were approached through Granger’s causality technique. The results depicted in

Granger causality test for different Sunflower markets

The study investigates the stationarity and cointegration in major groundnut, sunflower, and soybean oil markets of India and the international market. The study examines the market integration in five selected domestic markets and one international market for each selected crop using Johansen’s cointegration test and Granger Causality test. Unit root test showed non-stationary of price series at their levels, and it became stationary after the first differences. Johansen’s cointegration test has shown that even though the selected wholesale oil markets are geographically separated and spatially segmented, they are well-connected in terms of oil prices of all selected crops, demonstrating that the selected oil markets have long-run price linkages. The outcome of the Granger causality test, confirmed unidirectional and bidirectional causalities between the selected oilseed market pairs. In the case of groundnut, Rotterdam (International) market is influenced by only the Delhi market while all selected domestic markets influence the latter. The causality results in soybean markets affirmed the unidirectional influence of domestic markets on the international market except for Jaipur market, which has a bidirectional relationship with the international market. In the case of sunflower, only Hyderabad and Vijayawada markets are influencing the international market prices. The suggested policy intervention calls for faster movement of market information through strengthening market intelligence and establishing an online marketing system through networking. Development/ strengthening of market infrastructure, including communication, transportation, and storage facilities, is the need of the hour to integrate the market prices fully.