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Statements

Subject Item
n2:RIV%2F61989100%3A27510%2F10%3A10224832%21RIV11-GA0-27510___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
The Lévy models, when used to simulate time series of returns, enable us to model kurtosis and skewness and thus overcome the main drawback of the Brownian motion. In this paper we focus on the two most widely processes from the Lévy's family of models, a variance gamma and a normal inverse Gaussian model. The variance gamma model can be regarded as a subordinated (geometric) Brownian motion driven by a random time with gamma distribution. In the normal inverse Gaussian model, Brownian motion is driven by the inverse Gaussian distribution. Both of these models have four parameters, which need to be estimated. In the application part parameters for both models are estimated by means of a method of moments and a maximum likelihood method for five stock indices and five foreign exchange rates. Thereafter modeling quality of these estimated models is compared. Comparison is made on the basis of a log-likelihood function and errors of the basic descriptive statistics and quantile measures VaR a cVaR. The Lévy models, when used to simulate time series of returns, enable us to model kurtosis and skewness and thus overcome the main drawback of the Brownian motion. In this paper we focus on the two most widely processes from the Lévy's family of models, a variance gamma and a normal inverse Gaussian model. The variance gamma model can be regarded as a subordinated (geometric) Brownian motion driven by a random time with gamma distribution. In the normal inverse Gaussian model, Brownian motion is driven by the inverse Gaussian distribution. Both of these models have four parameters, which need to be estimated. In the application part parameters for both models are estimated by means of a method of moments and a maximum likelihood method for five stock indices and five foreign exchange rates. Thereafter modeling quality of these estimated models is compared. Comparison is made on the basis of a log-likelihood function and errors of the basic descriptive statistics and quantile measures VaR a cVaR.
dcterms:title
A modeling quality comparison of estimated Lévy models A modeling quality comparison of estimated Lévy models
skos:prefLabel
A modeling quality comparison of estimated Lévy models A modeling quality comparison of estimated Lévy models
skos:notation
RIV/61989100:27510/10:10224832!RIV11-GA0-27510___
n4:aktivita
n21:S n21:P
n4:aktivity
P(GA402/08/1237), S
n4:dodaniDat
n14:2011
n4:domaciTvurceVysledku
n15:3143783
n4:druhVysledku
n11:D
n4:duvernostUdaju
n18:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
244694
n4:idVysledku
RIV/61989100:27510/10:10224832
n4:jazykVysledku
n5:eng
n4:klicovaSlova
Lévy process, variance gamma model, normal inverse Gaussian model, parameter estimation
n4:klicoveSlovo
n13:variance%20gamma%20model n13:normal%20inverse%20Gaussian%20model n13:parameter%20estimation n13:L%C3%A9vy%20process
n4:kontrolniKodProRIV
[AF6751E507FD]
n4:mistoKonaniAkce
České Budějovice
n4:mistoVydani
České Budějovice
n4:nazevZdroje
Mathematical Methods in Economics 2010
n4:obor
n7:AH
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:projekt
n10:GA402%2F08%2F1237
n4:rokUplatneniVysledku
n14:2010
n4:tvurceVysledku
Kresta, Aleš
n4:typAkce
n8:EUR
n4:wos
000287979900063
n4:zahajeniAkce
2010-09-08+02:00
s:numberOfPages
6
n17:hasPublisher
University of South Bohemia
n12:isbn
978-80-7394-218-2
n20:organizacniJednotka
27510