Co-integration and error correction modelling for prices of black pepper in Karnataka

Introduction

The uncertainty about future prices has all through been a concern for producers and consumers. It has been observed that price discovery and price risk management are the two important functions performed by advance contracts (Thomas, 2003 and Ahuja, 2006). It could, therefore, be perceived that a fairly good idea of prices at a future date would facilitate producers' rational market decisions (especially regarding choice of market(s) and quantum of produce to be dispatched) for profit maximisation. Against this backdrop, market integration and price forecasting would help in stabilising the prices by removing the market imperfections like monopolies and attain market efficiency (Mushtaq et al., 2008) [7] . Spatial market integration "is the smooth transmission of price signals and information across spatially separated markets" (Golleti et al., 1995) or is the "measure of the extent to which demand and supply in one location are transmitted to another" (Negassa et al., 2003) [8] . If two markets are integrated, they will experience identical price shocks/changes (Barrett, 1996) [1] . Information flow between spot and future markets facilitates price fixation of commodity through mutual understanding between the buyer and the seller for which the price has to be paid in the pre-determined time in future. This reduces the chances of very high prices in return for protection against extremely low prices, technically called hedging, i.e. price risk management or risk transfer function. Price stabilization at the times of extreme volatility is another function of commodity futures. But, the issue over the past two decades is that prices of food are more volatile than of any other commodity (Chand, 2010) [2] . Escalating prices of food commodities question the sustainability of current economic growth as well as the efficiency of futures trading. In spite of these, growth in quantity and value of commodities traded in future markets has shown an increasing trend (Sen, 2008) [9] . Despite explicit growth, the active participation of farmer is also important. It helps them to hedge their produce, safeguard from price volatility and also signal the likely prices so that they can decide on their crop choices. In India, where the majority of farmers have low marketed surplus and face problem of price instability, the analysis of future trading and prescriptions become more relevant to them.

Materials and Methods

Co-integration

To test the extent of relationship between all Black pepper markets in Karnataka both in long run and short run, Cowww.extensionjournal.com integration was used. Markets are said to be integrated when the price change in one market is fully transmitted to other markets. Markets that are not integrated may convey inaccurate price information that might distort market decisions and contribute to inefficient product movement.

Augmented Dickey-Fuller (ADF) and Unit Root Test

An implicit assumption in Johansen's co-integration approach is that the variables should be non stationary at level, but stationary after first differencing. The Augmented Dickey-Fuller test is utilized to check the order of integration by using the model ( 1):where

and ΔY t -2 = Y t -2 -Y t -3, etc., ϵ t is pure white noise, term, α is the constant-term, T is the time trend effect, and p is the optimal lag value which is selected on the basis of Schwartz information criterion1 (SIC). The null hypothesis is that β 1 , the coefficient of Y t -1 is zero. The alternative hypothesis is: β 1 < 0. A non-rejection of the null hypothesis suggests that the time series under consideration is non-stationary.

Co-integration Analysis Using Johansen co integrating test

The Johansen procedure examines a vector auto regressive (VAR) model of Y t , an (n × 1) vector of variables that are integrated of the order one-I (1) time series. This VAR can be expressed as Equation Where, and are matrices of parameters, p is the number of lags (selected on the basis of Schwarz information criterion), is an (n × 1) vector of innovations. The presence of at least one co integrating relationship is necessary for the analysis of long-run relationship of the prices to be reasonable. To detect the number of cointegrating vectors, Johansen proposed two likelihood ratio tests (Dickey and Fuller, 1981) [3]: trace test and maximum Eigen value test, shown in Equations ( 3) and (4), respectively.

Where, T is the sample size and is the i th largest canonical correlation. The trace test examines the null hypothesis of r co integrating vectors against the alternative hypothesis of n co integrating vectors. The maximum Eigen value test, on the other hand, tests the null hypothesis of r cointegrating vectors against the alternative hypothesis of r+1 cointegrating vectors.

Results and Discussion

Co-integration of prices of Black Pepper in Different Markets of Karnataka

To test for extent of relationship between Bengaluru, Sirsi, Sakleshpura, Chikmagaluru and Somwarpet market prices both in long run also short run, co-integration was used. Figure 1. displays the Black Pepper prices for Bengaluru, Sirsi, Sakleshpura, Chikmagaluru and Somwarpet markets. Note that the Chikmagaluru market price is generally higher than the other markets during the testing period, whereas all the remaining markets prices were almost similar; difference is there because of addition of transportation cost, exchange rate difference, demand of Black pepper in world level and quality of product. In addition, as compared with the Chikmagaluru price, the other markets price has generally trend up over time with relatively low levels of volatility. If there is non stationary in the data it has to bring it to the stationary, thus series is subjected to first differencing to make the data stationary. The results of differenced series in table 2 indicated that the test statistic obtained for the entire five markets price is significant and less than at 1 percent level. So, we rejected the null hypothesis of stationary series and no unit root is true. The data series become stationary at one differencing and indicate that all the variables are integrated at order one.

Co integration test for Prices of all five Markets

Further when variables are stationary at same level I (1), check variables for long term relationship. Thus after unit root, Co integration test can be carried out to check for long term relationship. The Johansen's Co-integration test was carried out between all the five markets and the results are given in Table 3. The study has found number of Co-integrated equations using trace statistics and maximum Eigen value statistics. In this case, the number of co-integrating vectors can be at most four, as there are five series. According to probabilities given in table 3, the analysis rejects the null hypothesis that there is no Co-integrated vector (None), and accepts the null hypothesis that there are at most 2 Co-integrated vectors in both trace statistics and maximum eigenvalue statistics. It shows long run association between all the five markets price used in current study.

Conclusion

The high volatility in prices of agricultural commodities is a matter of concern for farmers and policy makers. With futures markets being blamed for food price inflation, the present study has found the efficiency of agricultural commodity futures in terms of price transmission, price discovery and degree of volatility in spot markets. Johansen's co integration test was used to know the co integration between the different markets of black pepper in Karnataka. The study has confirmed the presence of cointegration, implying the long-run price association among

Acknowledgement

www.extensionjournal.com the markets.

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