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This thesis compares Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Conditional Heteroscedacity (GARCH) models for three metal commodities. ARMA models have an unconditionally non-random and constant variance, which typically serves well in effectively representing homoscedastic data. The GARCH models feature variable variance that is non-random when conditioning on the past