An Optimal Generalized Autoregressive Conditional Heteroscedasticity Model for Forecasting the South African Inflation Volatility

Abstract

Abstract: In most cases, financial variables are explained by leptokurtic distribution and often fail the assumption of normal distribution. This paper sought to explore the robustness of GARCH–type models in forecasting inflation volatility using quarterly time series data spanning 2002 to 2014. The data was sourced from the South African Reserve Bank database. SAS version 9.3 was used to generate the results. The initial analyses of data confirmed non-linearity, hereroscedasticity and non-stationarity in the series. Differencing was imposed in a log transformed series to induce stationarity. Further findings confirmed that 𝐴𝑅 (1)_𝐼𝐺𝐴𝑅𝐶𝐻 (1, 1)model suggested a high degree persistent in the conditional volatility of the series. However, the𝐴𝑅 (1)_𝐸𝐺𝐴𝑅𝐶𝐻 (2, 1)model was found to be more robust in forecasting volatility effects than the 𝐴𝑅 (1)_𝐼𝐺𝐴𝑅𝐶𝐻 (1, 1) and 𝐴𝑅 (1)_𝐺𝐽𝑅 − 𝐺𝐴𝑅𝐶𝐻 (2, 1)models. This model confirmed that inflation rates in South Africa exhibits the stylised characteristics such as volatility clustering, leptokurtosis and asymmetry effects. These findings may be very useful to the industry and scholars who wish to apply models that capture heteroscedastic and non-linear errors. The findings may also benefit policy makers and may be referred to when embarking on strategies in-line with inflation rate.