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Savka_Thesis_Wavelet_2018.pdf (1.95 MB)

ETD Abstract Container

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Wavelet Transform in Financial Time Series Analysis: Denoising and Forecast

Savka, Andriy
http://orcid.org/0000-0001-5026-2912
http://rave.ohiolink.edu/etdc/view?acc_num=kent1543573160243739

Abstract Details

2018, MS, Kent State University, College of Arts and Sciences / Department of Mathematical Sciences.
Wavelet transform, based on the theory of Fourier transform, is a powerful tool of frequency analysis, which allows to switch from time domain of time series to its frequency-representation for further study. Wavelet transformation techniques are widely used in signal processing, utilized to compress and efficiently store signal and image information with minimum loss of important details. Most economic and financial time series contain layered information about trend of the related economic phenomena, seasonal variation, and noise. The latter is usually associated with unexplained uncertainty shocks. As these three components of economic or financial time series have different frequencies, it is natural to apply frequency analysis tools to extract useful information and reduce noise (unimportant component of time series). The purpose of this thesis is to review recent study on wavelet transform techniques and their applications for denoising in economic and financial time series. The thesis begins from overview of wavelets, their connection to Fourier transform, and place in frequency analysis study. Then, Dyadic multiresolution analysis as a basic framework of discrete wavelet analysis is discussed. Next, wavelet denoising is discussed. Further, statistical methods of time series analysis are introduced. The research concludes with empirical application of denoising technique using discrete wavelet transform to analysis of the Standard\&Poor's 500 stock prices index and West Texas Intermediate crude oil prices on the U.S. market.
Artem Zvavitch (Committee Chair)
Dmitry Ryabogin (Committee Member)
Peter Gordon (Committee Member)
64 p.
Mathematics

Recommended Citations

Citations

  • Savka, A. (2018). Wavelet Transform in Financial Time Series Analysis: Denoising and Forecast [Master's thesis, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1543573160243739

    APA Style (7th edition)

  • Savka, Andriy. Wavelet Transform in Financial Time Series Analysis: Denoising and Forecast. 2018. Kent State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1543573160243739.

    MLA Style (8th edition)

  • Savka, Andriy. "Wavelet Transform in Financial Time Series Analysis: Denoising and Forecast." Master's thesis, Kent State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1543573160243739

    Chicago Manual of Style (17th edition)

kent1543573160243739
767
© 2018, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.
Release 3.2.12