Analyzing the Cyclical Components of the S&P 500 Stock Index through Wavelet Transformation

Bozhidar Nedev () and Boryana Bogdanova
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Boryana Bogdanova: University of Sofia, Bulgaria

Ikonomiceski i Sotsialni Alternativi, 2019, issue 4, 95-110

Abstract: The paper systematizes the main scientific constellations when employing wavelet transformation. Thus, the advantages of the wavelet as a relatively new approach in comparison to the alternative methods of time-frequency analysis of time-series are presented. This innovative approach is being neglected by the economic scientific community as a whole due to the preference of applying traditional econometric techniques. Wavelet transformation can partition the time-series structure into simpler components that correspond to different investment horizons (frequencies). The main goal of the paper is to investigate the time-frequency characteristics of the volatility of the monthly close prices of S&P500 index for a period of 65 years. The applicability of the continuous wavelet transformation to one-dimensional analysis is also presented. The results show that the data structure undergoes severе structural changes within the analyzed period with a clear appearance of the Technological boom and the Mortgage crisis of 2007-2008

Keywords: wavelet transformation; capital markets; S&P 500; cyclical components; economic crisis (search for similar items in EconPapers)
JEL-codes: C02 C22 G01 G12 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:nwe:iisabg:y:2019:i:4:p:95-110

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