The Macroeconomic Determinants Of Volatility In Precious Metals Prices In Ethiopia Using Garch And Riskmetrics Models

Modelling and forecasting volatility for the price of precious metals has become a fertile field ofrnempirical research in financial markets. Since volatility is considered as an important concept inrnmany economic and financial applications. The objective of this study was to model and forecastrnthe volatility dynamics in precious metals prices in the Ethiopian market using GARCH familyrnand RiskMetrics models using data from January1998 to January 2014.rnThe price return series of gold and silver show the characteristics of financial time series such asrnleptokurtic distributions and thus, can suitably be modeled using EWMA and GARCH familyrnmodels. Empirical investigation was conducted in order to model price volatility using EWMArnand GARCH family models. Among the GARCH family models considered in this study,rnARMA (0, 1)-GARCH-M (2, 2) model with Student’s t-distributional assumption of residualsrnand ARMA (1, 3)-EGARCH (3, 2) model with normal distributional assumption of residualsrnwere found to be better fit for price volatility of gold and silver, respectively.rnSaving interest rate, exchange rate and price of crude oil were found to have statisticallyrnsignificant effect on monthly price volatility of gold. On the other hand, saving interest rate andrngeneral inflation rate have statistically significant effect on monthly price volatility of silver. Thernrisk premium effect for GARCH-M (2, 2) model was positive and statistically significant. Thisrnimplies that an increase in volatility would increase the mean return. The asymmetric term wasrnfound to be positive and significant in EGARCH (3, 2) volatility model for sliver. This is anrnindication that unanticipated increase in price had larger impact on price volatility thanrnunanticipated decrease in the price of silver.rnA comparison was made between GARCH family models and exponentially weighted movingrnaverage (EWMA) model. The study suggests that GARCH class of models appear to be better inrnvolatility forecasting than EWMA model as judged by RMSE and MAE criteria.rnKey words: EWMA model, GARCH model, Precious metals and Volatility

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