Analysis of main determinants of soya bean price volatility in Malawi

Thumbnail Image
Chimaliro, Aubrey Victor
Journal Title
Journal ISSN
Volume Title
African Economic Research Consortium
The study primarily focuses on analysing the extent of soybean price volatility in Malawi. The interest in the study was triggered by the findings from literature noting soybean prices in Malawi as being particularly volatile. Soybean is one of the most important oilseed crops in Malawi and has the potential to become a major export crop. It offers good export prospects to neighbouring countries in Southern Africa, but has also been prioritised for its potential domestic contribution in Malawi. It is regarded as one of the value chains that promotes better nutrition in Malawi since most diets are dominated by maize, which contributes to malnutrition in Malawi. The study empirically estimates soybean price volatility using a GARCH (1,1) model and results indicate that both the lagged squared residual and the lagged conditional variance have an effect on the conditional variance of soybean prices. GARCH terms are significant, indicating some volatility clustering in monthly returns of soybeans in Malawi, South Africa and the world. The study confirms that soybean prices in Malawi have been more volatile relative to South Africa and the USA. To evaluate the extent to which domestic soybean price volatility can be attributed to regional and global market volatility, the Engle-Granger procedure was employed to estimate long-run co-integration between soybean prices in Malawi, South Africa and the world. The prices are categorised into six pairs and testing the long-run co-integration between these pairs involve both directions. Five out of the six pairs of prices exhibit long-run co-integrating relationships. An error correction model (ECM) is also employed to estimate the speed of adjustment to the equilibrium for five co-integrated price series. South Africa is the fastest in responding to the USA price changes, taking two months. Malawi is the second fastest since it takes about four months for soybean prices to respond to shocks in South African markets. However, it takes about seven months for the USA soybean prices to respond to price shocks in the South African markets which is longer than the period that Malawi takes to respond. South Africa takes nine months to respond to the shocks that occur in the Malawian markets. USA is the lowest in terms of the speed of adjustment since it takes about sixteen months to respond to the Malawian market shocks. Therefore, this study agrees with expectation that changes in the international markets affect the domestic markets. This is so because South Africa is a small nation in the international soybean market and Malawi is even much smaller – the volumes traded in these markets are considered too small to have any meaningful impact on world market prices. Lastly, to evaluate the influence of shocks on the South African prices, as well as selected macro-economic variables in Malawi on soybean price volatility in Malawi, the study employs a vector error correction model (VECM) to evaluate the long- and short-run relationship between soybean prices and explanatory variables (South Africa soybean prices, exchange rates and consumer price index). The error correction coefficient of -0.2089 is negative and highly significant which is in line with expectations. The Johansen test points to the possibility of 1 or 2 cointegrated relationships between variables. The Wald test results show that the probability values of the joint F-statistics for South African soybean prices and Malawi exchange rate are significant at 10% and 5% levels respectively. Based on the significance of both the error correction term and the joint F-statistics of the two lagged variables in the Wald test results, the study concludes that South Africa soybean prices and Malawi exchange rate are significant drivers of volatility in Malawi soybean prices.
Price volatility, Price transmission, Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Vector Correction Model (VECM).