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dc.contributor.advisorBaruník, Jozef
dc.creatorAvdulaj, Krenar
dc.date.accessioned2017-04-21T08:07:23Z
dc.date.available2017-04-21T08:07:23Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/20.500.11956/31125
dc.description.abstractPosouzení extrémních jevů má zásadní význam pro řízení finančních rizik. Všechna rizikoví manažeři a finanční instituce chtějí znát riziko svého portfolia. Použiji metodu Monte Carlo a semi-parametrickou metody pro odhad Value-at-Risk (VaR) pro portfolio indexů burz ve střední Evropě.cs_CZ
dc.description.abstractAssessing the extreme events is crucial in financial risk management. All risk managers and and financial institutions want to know the risk of their portfolio under rare events scenarios. We illustrate a multivariate Monte Carlo and semi-parametric method to estimate Value-at-Risk (VaR) for a portfolio of stock exchange indexes in Central Europe. It is a method that uses the non-parametric empirical distribution to capture the small risks and the parametric Extreme Value theory to capture large risks. We compare this method with historical simulation and variance-covariance method under low and high volatility samples of data. In general historical simulation method over estimates the VaR for extreme events, while variance-covariance underestimates it. The method that we illustrate gives a result in between because it considers historical performance of the stocks and also corrects for the heavy tails of the distribution. We conclude that the estimate method that we illustrate here is useful in estimating VaR for extreme events, especially for high volatility times.en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Fakulta sociálních vědcs_CZ
dc.titleValue-at-risk based extreme value theory method and copulas : empirical evidence from Central Europeen_US
dc.typediplomová prácecs_CZ
dcterms.created2010
dcterms.dateAccepted2010-06-23
dc.description.departmentInstitute of Economic Studiesen_US
dc.description.departmentInstitut ekonomických studiícs_CZ
dc.description.facultyFaculty of Social Sciencesen_US
dc.description.facultyFakulta sociálních vědcs_CZ
dc.identifier.repId102576
dc.contributor.refereeSeidler, Jakub
dc.identifier.aleph001284643
thesis.degree.nameMgr.
thesis.degree.levelnavazující magisterskécs_CZ
thesis.degree.disciplineEkonomie a financecs_CZ
thesis.degree.disciplineEconomics and Financeen_US
thesis.degree.programEkonomické teoriecs_CZ
thesis.degree.programEconomicsen_US
uk.thesis.typediplomová prácecs_CZ
uk.taxonomy.organization-csFakulta sociálních věd::Institut ekonomických studiícs_CZ
uk.taxonomy.organization-enFaculty of Social Sciences::Institute of Economic Studiesen_US
uk.faculty-name.csFakulta sociálních vědcs_CZ
uk.faculty-name.enFaculty of Social Sciencesen_US
uk.faculty-abbr.csFSVcs_CZ
uk.degree-discipline.csEkonomie a financecs_CZ
uk.degree-discipline.enEconomics and Financeen_US
uk.degree-program.csEkonomické teoriecs_CZ
uk.degree-program.enEconomicsen_US
thesis.grade.csVýborněcs_CZ
thesis.grade.enExcellenten_US
uk.abstract.csPosouzení extrémních jevů má zásadní význam pro řízení finančních rizik. Všechna rizikoví manažeři a finanční instituce chtějí znát riziko svého portfolia. Použiji metodu Monte Carlo a semi-parametrickou metody pro odhad Value-at-Risk (VaR) pro portfolio indexů burz ve střední Evropě.cs_CZ
uk.abstract.enAssessing the extreme events is crucial in financial risk management. All risk managers and and financial institutions want to know the risk of their portfolio under rare events scenarios. We illustrate a multivariate Monte Carlo and semi-parametric method to estimate Value-at-Risk (VaR) for a portfolio of stock exchange indexes in Central Europe. It is a method that uses the non-parametric empirical distribution to capture the small risks and the parametric Extreme Value theory to capture large risks. We compare this method with historical simulation and variance-covariance method under low and high volatility samples of data. In general historical simulation method over estimates the VaR for extreme events, while variance-covariance underestimates it. The method that we illustrate gives a result in between because it considers historical performance of the stocks and also corrects for the heavy tails of the distribution. We conclude that the estimate method that we illustrate here is useful in estimating VaR for extreme events, especially for high volatility times.en_US
uk.file-availabilityV
uk.publication.placePrahacs_CZ
uk.grantorUniverzita Karlova, Fakulta sociálních věd, Institut ekonomických studiícs_CZ
dc.identifier.lisID990012846430106986


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