Wavelet methods possess some features that make them a tool with great potential for financial research. The purpose of this thesis is to study the usefulness of wavelet methods in financial time series analysis, for which data from Colombian financial market has been used. In this thesis the wavelet theory is briefly presented, with a special focus on the Discrete Wavelet Transform and Daubechies wavelets. Then, a multiresolution decomposition is illustrated for two distinct log-returns series. Finally, a wavelet-based prediction approach is presented, as well as a comparison between its results and those of a traditional prediction method.
