A Statistical Approach In Modelling Maize Prices Volatility

Volatility provides a measure of the possible variation or movement in the price of a commodity experienced over a given period of time . In this study, volatility of maize prices for Kenya, Uganda, and Tanzania in comparison with the FAO Global prices for the period of January 2003 to August 2011 is estimated. The study employs measures of dispersion like unconditional standard deviation and the coefficient of variation together with a 20% band around the trend line compared with time series models, the ARMA and EGARCH. Maize prices in Kenya, Tanzania, and FAO Global were found to be time varying and thus estimated using the EGARCH model and the average unconditional standard deviation used as a measure of volatility. Volatility of Uganda maize prices was constant over time and was estimated using ARMA (1,1). The study shows that by both methods, maize prices in Uganda were the most volatile followed by Kenya, Tanzania and then FAO Global implying that volatility in the East African markets is high compared to the global maize market.

Autorentext

Jonesmus is a medical statistician with the KEMRI-Wellcome Trust Research Programme where he is involved in developing Bayesian spatial-temporal models for modelling binomial and count data on Malaria. Previosly he worked at the International Livestock Research Institute as a Data Analyst.