  The assessment of co-movement among metals is crucial to better understand
the behaviors of the metal prices and the interactions with others that affect
the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector
Autoregressive Moving Average) models are utilized. First, Multiple Wavelet
Coherence (MWC), where Wavelet Analysis is needed, is utilized to determine
dynamic correlation time interval and scales. VARMA is then used for
forecasting which results in reduced errors.
  The daily prices of steel, aluminium, copper and zinc between 10.05.2010 and
29.05.2014 are analyzed via wavelet analysis to highlight the interactions.
Results uncover interesting dynamics between mentioned metals in the
time-frequency space. VARMA (1,1) model forecasting is carried out considering
the daily prices between 14.11.2011 and 16.11.2012 where the interactions are
quite high and prediction errors are found quite limited with respect to
ARMA(1.1). It is shown that dynamic co-movement detection via four variables
wavelet coherency analysis in the determination of VARMA time interval enables
to improve forecasting power of ARMA by decreasing forecasting errors.
