Date of Award
2023-05-01
Degree Name
Doctor of Philosophy
Department
Computational Science
Advisor(s)
Maria C. Mariani
Abstract
This dissertation aims to assess the performance of Ornstein-Uhlenbeck-type models by examining the fractal characteristics of time series data from various sources, including finance, volcanic and earthquake events, US COVID-19 reported cases and deaths, and two simulated time series with differing properties. The time series data is categorized as either a Gaussian or a Lévy process (Lévy walk or Lévy flight) by using three scaling methods: Rescaled range analysis, Detrended fluctuation analysis, and Diffusion entropy analysis. The outcomes of this analysis indicate that the financial indices are classified as Lévy walks, while the volcanic, earthquake, and COVID-19 data are classified as Lévy flights. The two simulated Brownian motions are classified as Gaussian processes, as expected. The simulation results of the time series using Ornstein-Uhlenbeck models emphasize the need for selecting an appropriate background driving process, combining solutions of Ornstein-Uhlenbeck-type SDEs, and considering the correlations between time series events to improve the performance of the Ornstein-Uhlenbeck-type models.
Language
en
Provenance
Recieved from ProQuest
Copyright Date
2023-05-01
File Size
p.
File Format
application/pdf
Rights Holder
Peter Kwadwo Asante
Recommended Citation
Asante, Peter Kwadwo, "Performance Classification Of Ornstein-Uhlenbeck-Type Models Using Fractal Analysis Of Time Series Data." (2023). Open Access Theses & Dissertations. 3760.
https://scholarworks.utep.edu/open_etd/3760
Included in
Finance and Financial Management Commons, Geophysics and Seismology Commons, Statistics and Probability Commons