| Summary: | An understanding of how volatilities of and correlations between commodity returns
change over time including their directions (positive or negative) and size (stronger or
weaker) is of crucial importance for both the domestic and international investors with a
view to diversifying their portfolios for hedging against unforeseen risks. This paper is an
humble attempt to add value to the existing literature by empirically testing the ‘timevarying’ and ‘scale dependent’ volatilities of and correlations of the sample commodities.
Particularly, by incorporating scale dependence, it is able to identify unique portfolio
diversification opportunities for different set of investors bearing different investment
horizons or holding periods. In order to address the research objectives, we have applied
the vector error-correction test and several recently introduced econometric techniques
such as the Maximum Overlap Discrete Wavelet Transform (MODWT), Continuous
Wavelet Transform (CWT) and Multivariate GARCH – Dynamic Conditional
Correlation. The data used in this paper is the daily data of seven commodities (crude oil,
gas, gold, silver, copper, soybean and corn) prices from 1 January 2007 until 31
December 2013.
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