(eng): 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 separate- ly. 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 correlations. In the empirical analysis, we connect our approach to the dynamic conditional correla- tion approach of Engle (2002) on the main components of the energy sector. Namely, we use crude oil, gas- oline, 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.