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The assessment of co-movement among metals is crucial to better understandthe behaviors of the metal prices and the interactions with others that affectthe changes in prices. In this study, both Wavelet Analysis and VARMA (VectorAutoregressive Moving Average) models are utilized. First, Multiple WaveletCoherence (MWC), where Wavelet Analysis is needed, is utilized to determinedynamic correlation time interval and scales. VARMA is then used forforecasting which results in reduced errors.  The daily prices of steel, aluminium, copper and zinc between 10.05.2010 and29.05.2014 are analyzed via wavelet analysis to highlight the interactions.Results uncover interesting dynamics between mentioned metals in thetime-frequency space. VARMA (1,1) model forecasting is carried out consideringthe daily prices between 14.11.2011 and 16.11.2012 where the interactions arequite high and prediction errors are found quite limited with respect toARMA(1.1). It is shown that dynamic co-movement detection via four variableswavelet coherency analysis in the determination of VARMA time interval enablesto improve forecasting power of ARMA by decreasing forecasting errors.

Multiple Wavelet Coherency Analysis and Forecasting of Metal Prices