Statistical Analysis of Domestic Price Volatility of Sugar in Ethiopia

Abstract

The aim of this study was to model and identify determinants of monthly domestic price volatility of sugar in Ethiopia over the study period from December 2001 to December 2011 GC. The volatility in the domestic price of Sugar has been found to vary over months suggesting the use of GARCH family approach. Thus, family of special characteristics of time series models, namely ARCH, GARCH, TGARCH and EGARCH models with ARIMA mean equations were fitted to the data. The best fitting model among each family of models was selected based on how well the model captures the variation in the data and the optimal lag specification accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric model were carried out based on the significance of asymmetric term in TGARCH and EGARCH models. The analysis showed that: statistically significance asymmetric term and least forecast error from the model established that EGARCH model with Student-t distributional assumptions for residual were superior to the GARCH and TGARCH models. Therefore, ARIMA (0,0,2)-EGARCH(1,3) with Student-t were chosen to be the best fitting models for monthly domestic price volatility of Sugar. Moreover, it was found that from candidate explanatory variables, import price for sugar, fuel oil price, exchange rate (dollar-birr), general inflation, inflation for non food items, inflation for food items, past shock, and volatility on monthly domestic price had statistically significant effect on the current month domestic price volatility on sugar.

Modeling and forecasting the volatility of some industry development indicators in Ethiopia using multivariate GARCH models

“…natural logarithm transformed and differenced series) at time t and is the estimated average return over period . Since variance is the square of standard deviation, it makes no difference which ever measure S or to compare volatilities 47 . Most financial studies involved returns instead of original series.…”

Modeling and Forecasting the Volatility of Some Industry Development Indicators in Ethiopia Using Multivariate GARCH Models

“…natural logarithm transformed and differenced series) at time t and 𝜇 is the estimated average return over period 𝑇. Since variance is the square of standard deviation, it makes no difference which ever measure S or 𝑆 2 to compare volatilities (Godana et al, 2014). Most financial studies involved returns instead of original series.…”

An analytical approximation of option prices via TGARCH model

“…Following Zakoian (1994), some researchers have studied and used the TGARCH model to estimate the volatility of other underlying asset prices, such as carbon, crude oil, ethanol, natural gas, coal-three, corn and sugar; see Alberola et al (2008); Hasan et al (2013);Trujillo-Barrera et al (2012) and Godana et al (2014) Moreover, the TGARCH model is used to describe mortgage risk factors of housing price and capture the house price dynamic on the logarithmic return and to estimate the housing price volatility for the pricing reverse mortgage derivatives (Lee et al, 2015). For financial market research, Sabiruzzaman et al (2010) investigated the pattern of volatility in the daily trading volume index of the Hong Kong stock exchange by using two approaches; GARCH and TGARCH models.…”