Term of Award
Fall 2017
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Department
Department of Mathematical Sciences
Committee Chair
Tharanga Wickramarachchi
Committee Member 1
Stephen Carden
Committee Member 2
Arpita Chatterjee
Abstract
This thesis explores how arc length can be modeled and used to measure the risk involved with a financial time series. Having arc length as a measure of volatility can help an investor in sorting which stocks are safer/riskier to invest in. A Gamma autoregressive model of order one(GAR(1)) is proposed to model arc length series. Kernel regression based bias correction is studied when model parameters are estimated using method of moment procedure. As an application, a model-based clustering involving thirty different stocks is presented using k-means++ and hierarchical clustering techniques.
Recommended Citation
Hoerlein, Benjamin H., "Modeling Volatility of Financial Time Series Using Arc Length" (2017). Electronic Theses and Dissertations. 1671.
https://digitalcommons.georgiasouthern.edu/etd/1671
Research Data and Supplementary Material
No
Included in
Longitudinal Data Analysis and Time Series Commons, Numerical Analysis and Scientific Computing Commons, Statistical Models Commons
