Abstract
ARCH (Autoregressive Conditional Heteroskedacity) and GARCH (Generalized Autoregressive Conditional Heteroskedacity) models have been used in forecasting fluctuations in exchange rates, commodities and securities and are appropriate for modeling time series in which there is non-constant variance, and in which the variance at one time period is dependent on the variance at a previous time period. In our paper we deal with APARCH models (Arithmetic Power Autoregressive Conditional Heteroskedasticity) in order to fit into a data series with asymmetric characteristics. We use Kenyan, Tanzanian and Mozambican data and perform the time series analysis and obtain a model that characterize the data set under consideration. Journal of the Institute of Engineering, 2018, 14(1): 96-106
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