Macroeconomic Determinants of Volatility for the Gold Price in Ethiopia: The Application of GARCH and EWMA Volatility Models

Author:

Ayele Amare Wubishet1,Gabreyohannes Emmanuel2,Tesfay Yohannes Yebabe3

Affiliation:

1. Department of Statistics, College of Natural and Computational Sciences, Debre Markos University, Debre Markos Ethiopia.

2. Associate Professor, Mathematics and Statistics Department, Ethiopian Civil Service University, Addis Ababa, Ethiopia.

3. Molde University College Molde, Britveien, Molde, Norway.

Abstract

Modelling and forecasting of commodity price volatility has important applications for asset management, portfolio analysis and risk assessment due to the simple fact that volatility has informational content and contains signals of the market information flow. This article models and forecasts the gold price volatility using the exponentially weighted moving average (EWMA) and the generalized autoregressive conditional heteroscedasticity (GARCH) models for the period from 1998 to 2014. The gold series shows the classical characteristics of financial time series, such as leptokurtic distributions, data dependence and strong serial correlation in squared returns. Hence, the series can be modelled using both EWMA and GARCH-type models. Among the GARCH-type models, GARCH-M(2,2) with Student’s t distribution for the residuals was found to be the best-fit model. Moreover, the manuscript finds that interest rates, exchange rates and crude oil prices have a significant impact on gold volatility. The risk premium effect is found to be positive and statistically significant, suggesting increased volatility is followed by a higher mean. Finally, a comparison is made between the GARCH and the EWMA models. Using the relative mean squared error and mean absolute error measures, the empirical result suggests that GARCH models with explanatory variables are superior for volatility forecasting.

Publisher

SAGE Publications

Subject

Business and International Management

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