Abstract
Today most of the available econometrics techniques exercise time series data setup either in time or frequency domain. In the real world financial markets deal with several interacting agents and to understand such complex interacting systems a single aspect provides incomplete information. The wavelet analysis made it thinkable to study the time series both in the time frequency domain. The Wavelet analysis made it possible to see the hidden interactions among the two time series that are hard to observe through any other possible available advanced econometric tools and moreover wavelet analysis is a model free approach. This chapter covers the theory on Wavelets and its importance on handling non normal time series data. Broadly it covers all of these following: Multi scale Wavelet decomposition; Wavelet Coherence; and Wavelet Clustering. Step wise execution with appropriate examples is discussed using the R programming. The wavelet analysis is very important to understand the co-movement among the time series and beneficial for portfolio diversification.
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