  This study attempts to investigate into the structure and features of global
equity markets from a time-frequency perspective. An analysis grounded on this
framework allows one to capture information from a different dimension, as
opposed to the traditional time domain analyses, where multiscale structures of
financial markets are clearly extracted. In financial time series, multiscale
features manifest themselves due to presence of multiple time horizons. The
existence of multiple time horizons necessitates a careful investigation of
each time horizon separately as market structures are not homogenous across
different time horizons. The presence of multiple time horizons, with varying
levels of complexity, requires one to investigate financial time series from a
heterogeneous market perspective where market players are said to operate at
different investment horizons. This thesis extends the application of
time-frequency based wavelet techniques to: i) analyse the interdependence of
global equity markets from a heterogeneous investor perspective with a special
focus on the Indian stock market, ii) investigate the contagion effect, if any,
of financial crises on Indian stock market, and iii) to study fractality and
scaling properties of global equity markets and analyse the efficiency of
Indian stock markets using wavelet based long memory methods.
