Date of Award
5-2-2018
Document Type
Thesis
Degree Name
Mathematics, MS
First Advisor
Doo Young Kim
Committee Members
Ferebee Tunno; Hong Zhou
Call Number
LD251.A566t 2018 K37
Abstract
This thesis presents a comprehensive report of my research in financial time series analysis from Fall 2017 to Spring 2018. It is focused on two main topics: clustering and modeling of financial time dependent information. The weighted five-day moving arc length is used as a measure of volatility, and we applied self-organizing maps to cluster the subject information. We perform a clustering procedure of financial time dependent information using several lag values, and Apple Incorporation and Google as the leading stocks. As a result, we discovered that there are others financial time dependent information present in the same cluster with them at different lag values. On the basis of this financial time dependent information, we proceed with the statistical modeling of financial time dependent information by using the restricted vector autoregressive model (RVAR).
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Karki, Ranju, "Statistical Modeling of Financial Time Series" (2018). Student Theses and Dissertations. 521.
https://arch.astate.edu/all-etd/521
