2002), ‘Use of Time-Frequency Representation in the Analysis of Stock Market Data

The analysis of economic/financial time series in the frequency domain is a relatively underexplored area of the literature, particularly when the statistical properties of a time series are time-variant (evolutionary). In this case, the spectral content of the series varies as time progresses, rendering the conventional Fourier theory inadequate to fully describe the cyclical characteristics of the series. The joint Time-Frequency Representation (TFR) techniques overcome this problem as they are capable of analyzing a given function of time (continuous or discrete) in time domain and in frequency domain simultaneously. The purpose of this paper is to illustrate the potential of some of the TFR techniques widely used in various fields of science and engineering, for use in the analysis of stock market data and to compare their performances. For this purpose, we analyze the behavior of ISE-100 index of Istanbul Stock Exchange over the period from July 9, 1990 to December 25, 2000 using two linear (the Gabor Transformation and the Short Time Fourier Transform) and two quadratic (the Wigner Distributio