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A comparative study of VaR and ES using extreme value theory

Andersson, Klara LU (2020) NEKH01 20201
Department of Economics
Abstract
Using data from OMXS30, we study which of the models block maxima and peaks-over-threshold, based on extreme value theory, are the most accurate when estimating the risk measures Value-at-Risk and Expected Shortfall. To perform this analysis, the risk measures are backtested. The extreme observations are fitted to the generalized extreme value distribution and the generalized Pareto distribution using maximum likelihood estimation. This study conclude that when estimating Value at Risk, block maxima is a more accurate model. When estimating Expected Shortfall, the difference between the models is not statistical significant.
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author
Andersson, Klara LU
supervisor
organization
alternative title
En jämförelsestudie av VaR och ES med hjälp av extremvärdesteori
course
NEKH01 20201
year
type
M2 - Bachelor Degree
subject
keywords
extreme value theory, block maxima, peaks-over-threshold, backtesting, value-at-risk, expected shortfall
language
English
id
9013722
date added to LUP
2020-08-29 11:22:46
date last changed
2020-08-29 11:22:46
@misc{9013722,
  abstract     = {{Using data from OMXS30, we study which of the models block maxima and peaks-over-threshold, based on extreme value theory, are the most accurate when estimating the risk measures Value-at-Risk and Expected Shortfall. To perform this analysis, the risk measures are backtested. The extreme observations are fitted to the generalized extreme value distribution and the generalized Pareto distribution using maximum likelihood estimation. This study conclude that when estimating Value at Risk, block maxima is a more accurate model. When estimating Expected Shortfall, the difference between the models is not statistical significant.}},
  author       = {{Andersson, Klara}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{A comparative study of VaR and ES using extreme value theory}},
  year         = {{2020}},
}