“A STATISTICAL ANALYSIS ON MARKET INTEGRATION AND PRICE FORECASTING OF DOMESTIC AND INTERNATIONAL WHEAT MARKETS”, T3217
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Date
2021-01
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JAU JUNAGADH
Abstract
Wheat (Triticum spp.) is one such important commodity in India that enjoys at
least 40 per cent procurement under minimum support price (MSP) mechanism. Despite
that, farmers outside the procurement bracket fail to realize remunerative prices in the
open markets. In this connection, marketing plays an important role in the economic
development as it stimulates production and avoids unnecessary fluctuation in output.
The Market integration analyses were also carried out by using Granger
bivariate co-integration test and Johansen multivariate co-integration test. These tests
revealed long-run equilibrium between these markets and the causality test indicates
the existence of feedback relationship between Rajkot, Junagadh, Mathura (UP),
Khanna (Punjab), Argentina and USA markets. As a preliminary, the Augmented
Dickey-Fuller test were conducted and it detected the presence of unit root in the price
series at levels for all the three markets and the stationary, which indicate price
transmission occur between the markets. The Vector Error Correction model (VECM)
captured the short-run adjustment towards the long-run equilibrium between the
markets and the adequacy of the fitted VECM is also checked. Granger causality test
was attempted to determine the direction of price influence in-between the market pairs.
The findings revealed bidirectional causality among all the local and national pairs. On
the flip side, the international markets were completely devoid of any type of causality
with domestic markets. Price transmission occur effectively and there was also a well
infrastructure facility to meet the proper good allocation in the studied markets.
The volatility and market integration analysis were carried out in selected wheat
markets from January, 2003 to December, 2019 by using various statistical models
available in the time series literature. Volatility in these markets were analyzed by using
the novel univariate autoregressive integrated moving-average (ARIMA) model,
conditional heteroscedsatic GARCH model and Vector Auto regression (VAR) model.
Price forecasting was attempted using ARIMA, ARCH/ GARCH and VAR models and
the results were compared. The following models were found to be fit in case of
ARIMA: Rajkot (2,1,2); Junagadh (3,1,2); Mathura (2,1,1); Khanna (1,1,1); USA
(2,1,3); and Argentina (2,1,2) on the basis of minimum value of MAPE, AIC, BIC and
higher value of adj. R2
. Cross-validation (i.e. out of sample forecasts) for the 12 month
period from Jan. 2019 to Dec. 2019 revealed the robustness of the selected models as
the forecasting errors in all the cases were found to be <10 per cent. GARCH effect was
found valid only in Khanna (Punjab) market and VAR was attempted for all the
markets. On the basis of forecasting criteria, ARIMA was found to be best suitable
when compared to VAR in all the markets