Dissertation/Thesis Abstract

Different estimations of time series models and application for foreign exchange in emerging markets
by Wang, Jingjing, M.S., Mississippi State University, 2016, 45; 10141678
Abstract (Summary)

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.

Indexing (document details)
Advisor: Wu, Tung-Lung
Commitee: Johnson, Corlis P., McBride, Matthew S., Miller, Thomas Len, Sepehrifar, Mohammad
School: Mississippi State University
Department: Mathematics and Statistics
School Location: United States -- Mississippi
Source: MAI 55/06M(E), Masters Abstracts International
Subjects: Statistics, Finance, Industrial engineering
Keywords: Emerging markets, Foreign exchange, Time series
Publication Number: 10141678
ISBN: 978-1-339-97038-7
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