Theses and Dissertations

Author

Jingjing Wang

Issuing Body

Mississippi State University

Advisor

Wu, Tung-Lung

Committee Member

McBride, Matthew S.

Committee Member

Sepehrifar, Mohammad

Date of Degree

1-1-2016

Document Type

Graduate Thesis - Open Access

Abstract

Time series models have been widely used in simulating financial data sets. Finding a nice way to estimate the parameters is really important. One of the traditional ways is to use maximum likelihood estimation to make an approach. However, when the error terms don’t have normality, MLE would be less efficient. Quasi maximum likelihood estimation, also regarded as Gaussian MLE, would be more efficient. Considering the heavy-tailed financial data sets, we can use non-Gaussian quasi maximum likelihood, which needs less assumptions and conditions. We use real financial data sets to compare these estimators.

URI

https://hdl.handle.net/11668/19994

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