Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
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This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
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Typical General Autoregressive Conditional Heteroskedastic (GARCH) processes involve normally-distributed errors, and they model strictly-positive error processes poorly. This thesis will present a method for estimating the parameters of a GARCH(1,1) process with shifted Gamma-distributed errors, conduct a simulation study to test the method, and apply the method to real time series data.
Budd, Alan C., "GARCH(1,1) with Sifted Gamma-distributed Errors" (2016). Electronic Theses and Dissertations. 1409.