Date of Award
2023-05-01
Degree Name
Doctor of Philosophy
Department
Computational Science
Advisor(s)
Maria C. Mariani
Abstract
The study uses various methods to compare financial and geophysical time series scaling parameters and long-term memory behavior. The Cantor Detrended Fluctuation Analysis (CDFA) method is proposed to provide more accurate estimates of Hurst exponents. The CDFA method is applied to real-time series and the results are verified. The study also analyzes the memory behavior of daily Covid-19 cases before and after the announcement of effective vaccines. Low and high-frequency dataâ??s influence on the Hurst Index estimation is investigated, and a new PCDFA method is proposed. The stability of the Dow Jones Industrial Average is analyzed using a multi-scale normalized diffusion entropy and conditional diffusion entropy. The study aims to investigate memory behavior in time series using deep learning techniques in future work.
Language
en
Provenance
Recieved from ProQuest
Copyright Date
2023-05-01
File Size
p.
File Format
application/pdf
Rights Holder
William Kubin
Recommended Citation
Kubin, William, "Volatility Modeling Of Time Series Using Fractal And Self-Similarity Models" (2023). Open Access Theses & Dissertations. 3808.
https://scholarworks.utep.edu/open_etd/3808