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Risk measure estimation in finance Open Access


Other title
Financial futures -- Risk management -- Mathematical models
Financial risk management -- Mathematical models
Finance -- Risk management -- Mathematical models
Type of item
Degree grantor
University of Alberta
Author or creator
Wang, Xupeng
Supervisor and department
Mizera, Ivan (Mathematical and Statistical Sciences)
Examining committee member and department
Hooper, Peter (Mathematical and Statistical Sciences)
Galvani, Valentina (Economics)
Department of Mathematical and Statistical Sciences

Date accepted
Graduation date
Master of Science
Degree level
In financial market, risk management is very critical to a company. However, some risks in the market ( market risk) can not be controlled or eliminated through management improvement or appropriate asset allocation. Thus, it is important to accurately measure these kinds of risks. In this thesis, we introduce two most widely used risk measures: value-at-risk and expected shortfall. Their estimation from data is the issue we are concerned with in this thesis. We divide this thesis into two parts: First, we survey the currently used estimation methods. We introduce these methods from the theoretical backgrounds. Then, we propose some criteria used to judge the performance of these methods. Second, we apply all these methods to data. We use the criteria introduced to compare these methods. This empirical study can shed some light on the application of these methods, bringing us some guidelines about their use in the future.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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