  In this paper, we contribute to the literature on energy market co-movement
by studying its dynamics in the time-frequency domain. The novelty of our
approach lies in the application of wavelet tools to commodity market data. A
major part of economic time series analysis is done in the time or frequency
domain separately. Wavelet analysis combines these two fundamental approaches
allowing study of the time series in the time- frequency domain. Using this
framework, we propose a new, model-free way of estimating time-varying cor-
relations. In the empirical analysis, we connect our approach to the dynamic
conditional correlation approach of Engle (2002) on the main components of the
energy sector. Namely, we use crude oil, gasoline, heating oil, and natural gas
on a nearest-future basis over a period of approximately 16 and 1/2 years
beginning on November 1, 1993 and ending on July 21, 2010. Using wavelet
coherence, we uncover interesting dynamics of correlations between energy
commodities in the time-frequency space.
