  We apply a recently developed wavelet based approach to characterize the
correlation and scaling properties of non-stationary financial time series.
This approach is local in nature and it makes use of wavelets from the
Daubechies family for detrending purpose. The built-in variable windows in
wavelet transform makes this procedure well suited for the non-stationary data.
We analyze daily price of NASDAQ composite index for a period of 20 years, and
BSE sensex index, over a period of 15 years. It is found that the long-range
correlation, as well as fractal behavior for both the stock index values differ
from each other significantly. Strong non-statistical long-range correlation is
observed in BSE index, whose removal revealed a Gaussian random noise character
for the corresponding fluctuation. The NASDAQ index, on the other hand, showed
a multifractal behavior with long-range statistical correlation.
