Term of Award
2011
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Patricia Humphrey
Committee Member 1
J. Duggins
Committee Member 2
B. Oluyede
Abstract
A time series is a sequence of data points, typically measured at uniform time intervals. Examples occur in a variety of fields ranging from economics to engineering, and methods of analyzing time series constitute an important part of Statistics. Time series analysis comprises methods for analyzing time series data in order to extract meaningful characteristics of the data and forecast future values. The Autoregressive Integrated Moving Average (ARIMA) models, or Box-Jenkins methodology, are a class of linear models that are capable of representing stationary as well as nonstationary time series. ARIMA models rely heavily on autocorrelation patterns. This paper will explore the application of the Box-Jenkins approach to stock prices, in particular sampling at different time intervals in order to determine if there is some optimal frame and if there are similarities in autocorrelation patterns of stocks within the same industry.
Recommended Citation
Green, Shakira, "Time Series Analysis of Stock Prices Using the Box-Jenkins Approach" (2011). Electronic Theses and Dissertations. 668.
https://digitalcommons.georgiasouthern.edu/etd/668
Research Data and Supplementary Material
No
