2006/01/31 by Prasanta K. Panigrahi, Manimaran, P., Panigrahi, Prasanta K. +2
Economics, Econometrics and Finance · Physics and Astronomy · #Chaos control and synchronization #Chaotic Dynamics (nlin.CD) #Complex Systems and Time Series Analysis #FOS: Economics and business #FOS: Physical sciences #Financial Risk and Volatility Modeling #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.nlin/0601074
openalex publication_date 2006/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.