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master's thesis
Semiparametarska procjena VaR-a
Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2017. urn:nbn:hr:126:494187
Novak, Tajana
Josip Juraj Strossmayer University of Osijek
Department of Mathematics
Chair of Pure Mathematics and Mathematics Teaching
Probability, Mathematical Statistics and Data Science Research Group

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Novak, T. (2017). Semiparametarska procjena VaR-a (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:494187

Novak, Tajana. "Semiparametarska procjena VaR-a." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2017. https://urn.nsk.hr/urn:nbn:hr:126:494187

Novak, Tajana. "Semiparametarska procjena VaR-a." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2017. https://urn.nsk.hr/urn:nbn:hr:126:494187

Novak, T. (2017). 'Semiparametarska procjena VaR-a', Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, accessed 23 September 2023, https://urn.nsk.hr/urn:nbn:hr:126:494187

Novak T. Semiparametarska procjena VaR-a [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics; 2017 [cited 2023 September 23] Available at: https://urn.nsk.hr/urn:nbn:hr:126:494187

T. Novak, "Semiparametarska procjena VaR-a", Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, Osijek, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:126:494187

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Metadata
TitleSemiparametarska procjena VaR-a
AuthorTajana Novak
MentorNenad Šuvak (mentor)
GranterJosip Juraj Strossmayer University of Osijek
Department of Mathematics
(Chair of Pure Mathematics and Mathematics Teaching)
(Probability, Mathematical Statistics and Data Science Research Group)
Osijek
Defense date and country2017-10, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Probability Theory and Statistics
Abstract
VaR je mjera rizika koja pruža najbolji omjer dobre teorijske podloge i korisnosti implementacije. Njene slabosti su nedostatak subaditivnosti za neke vrste imovine te činjenica da je samo kvantil distribucije povrata i referira samo minimalni gubitak uz zadanu vjerojatnost. Teorija ekstremnih vrijednosti može se koristiti za modeliranje mjera rizika kao što je value at risk, a primjenjujemo ju na povrate. Takva procjena VaR-a jest semiparametarska. EVT može biti korisna za procjenu veličine ekstremnih događaja, pritom možemo koristiti metodu maksimuma po segmentima ili metodu vrijednosti iznad praga. Metoda vrijednosti iznad praga, koja se oslanja na GPD teorijsku repnu funkciju distribucije, nije se pokazala dobra na promatranim podacima.
Abstract (english)
VaR is a risk measure that provides the best ratio of good theoretical background and usefulness of implementation. It’s weaknesses are the lack of subadditivity or some types of assets and the fact that it is only a quantile and refers only to a minimum loss with a given probability. Extreme value theory can be used to model risk measures such as value at risk and we apply it to returns. Such value at risk estimation is semiparametric. EVT can be useful for estimating the size of extreme events, while we can use block maxima or peaks over threshold method. The peaks over threshold method, which relies on fitted GPD tail distribution, didn’t proved to be good on the observed data.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:494187
Study programmeTitle: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics
Course: Financial and Statistical Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryRepository of School of Applied Mathematics and Computer Science
Created on2017-10-06 08:06:26