dc.contributor.advisor | Baruník, Jozef | |
dc.creator | Avdulaj, Krenar | |
dc.date.accessioned | 2017-04-21T08:07:23Z | |
dc.date.available | 2017-04-21T08:07:23Z | |
dc.date.issued | 2010 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11956/31125 | |
dc.description.abstract | Posouzení 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.abstract | Assessing 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.language | English | cs_CZ |
dc.language.iso | en_US | |
dc.publisher | Univerzita Karlova, Fakulta sociálních věd | cs_CZ |
dc.title | Value-at-risk based extreme value theory method and copulas : empirical evidence from Central Europe | en_US |
dc.type | diplomová práce | cs_CZ |
dcterms.created | 2010 | |
dcterms.dateAccepted | 2010-06-23 | |
dc.description.department | Institute of Economic Studies | en_US |
dc.description.department | Institut ekonomických studií | cs_CZ |
dc.description.faculty | Faculty of Social Sciences | en_US |
dc.description.faculty | Fakulta sociálních věd | cs_CZ |
dc.identifier.repId | 102576 | |
dc.contributor.referee | Seidler, Jakub | |
dc.identifier.aleph | 001284643 | |
thesis.degree.name | Mgr. | |
thesis.degree.level | navazující magisterské | cs_CZ |
thesis.degree.discipline | Ekonomie a finance | cs_CZ |
thesis.degree.discipline | Economics and Finance | en_US |
thesis.degree.program | Ekonomické teorie | cs_CZ |
thesis.degree.program | Economics | en_US |
uk.thesis.type | diplomová práce | cs_CZ |
uk.taxonomy.organization-cs | Fakulta sociálních věd::Institut ekonomických studií | cs_CZ |
uk.taxonomy.organization-en | Faculty of Social Sciences::Institute of Economic Studies | en_US |
uk.faculty-name.cs | Fakulta sociálních věd | cs_CZ |
uk.faculty-name.en | Faculty of Social Sciences | en_US |
uk.faculty-abbr.cs | FSV | cs_CZ |
uk.degree-discipline.cs | Ekonomie a finance | cs_CZ |
uk.degree-discipline.en | Economics and Finance | en_US |
uk.degree-program.cs | Ekonomické teorie | cs_CZ |
uk.degree-program.en | Economics | en_US |
thesis.grade.cs | Výborně | cs_CZ |
thesis.grade.en | Excellent | en_US |
uk.abstract.cs | Posouzení 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.en | Assessing 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-availability | V | |
uk.publication.place | Praha | cs_CZ |
uk.grantor | Univerzita Karlova, Fakulta sociálních věd, Institut ekonomických studií | cs_CZ |
dc.identifier.lisID | 990012846430106986 | |