Analysis of time series and stock market behavior using wavelet methods
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Date
2008-05
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
Post liberalization, the development of new techniques and ideas in econometrics have been rapidly growing over the last few years. These developments are now being applied to a wide variety of fields and in analyzing the stock market behavior. Specially the area of analysis of financial time series is catching the attention of big business houses as they are now contemplating on optimizing the business process in order to achieve best estimate of production planning over the long span of time and also to sustain the growth momentum. Economic and financial time series are nonstationary in nature and exhibits changing frequency patterns over the time. Wavelet analysis is one such tool for analyzing non-stationary data. Parameterization of wavelet families allows one to generate infinite number of wavelets for the different choices of selection for analyzing the financial time series. In present work, “Analysis of time series and stock market behavior using wavelet methods” we have proposed a simpler method to generate parametric families of orthogonal wavelet and used it to generate the ‘6-tap Daubechies wavelet filter’ in a straight forward manner. The wavelet based concepts have then been employed to study BSE and NSE indexes financial time series using index data from April 1990 to March 2006 by decomposing index based financial time series into time-scale components using the MODWT (Maximal Overlap Discrete Wavelet Transform) analysis. The most commonly used measure to analyze the stock market behavior is the wavelet correlation analysis, we have used it for the analysis of BSE and NSE indexes financial time series. The work embodied in this thesis serves as intuitive guide for analyzing the time series and can be of use for the predictions based on it by using the newly developed wavelet technique.
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