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  • During last decades the stochastic simulation approach, both via Monte Carlo (MC) and Quasi Monte Carlo (QMC) has been vastly applied and subsequently analyzed in almost all branches of science. Very nice applications can be found in areas that rely on modeling via stochastic processes, such as finance. However, since financial quantities, opposed to natural processes, depends on human activity, their modeling is often very challenging. Many scholars therefor suggest to specify some parts of financial models by means of fuzzy set theory. Since many financial problems are too complex to be solved analytically even in a crisp case, it can be efficient to apply (Quasi) Monte Carlo simulation. In this contribution the recent knowledge of fuzzy numbers and their approximation is utilized in order to suggest fuzzy-MC simulation to modeling of returns of financial quantities, such as prices of stocks, commodities or exchange rates.
  • During last decades the stochastic simulation approach, both via Monte Carlo (MC) and Quasi Monte Carlo (QMC) has been vastly applied and subsequently analyzed in almost all branches of science. Very nice applications can be found in areas that rely on modeling via stochastic processes, such as finance. However, since financial quantities, opposed to natural processes, depends on human activity, their modeling is often very challenging. Many scholars therefor suggest to specify some parts of financial models by means of fuzzy set theory. Since many financial problems are too complex to be solved analytically even in a crisp case, it can be efficient to apply (Quasi) Monte Carlo simulation. In this contribution the recent knowledge of fuzzy numbers and their approximation is utilized in order to suggest fuzzy-MC simulation to modeling of returns of financial quantities, such as prices of stocks, commodities or exchange rates. (en)
Title
  • Simulation methodology for financial assets with imprecise data
  • Simulation methodology for financial assets with imprecise data (en)
skos:prefLabel
  • Simulation methodology for financial assets with imprecise data
  • Simulation methodology for financial assets with imprecise data (en)
skos:notation
  • RIV/61988987:17610/11:A12012TV!RIV12-MSM-17610___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6198898701)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 229402
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17610/11:A12012TV
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Fuzzy variable; Stochastic variable; Fuzzy-stochastic variable; Financial models; Risk estimation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [0C8BC5E83368]
http://linked.open...v/mistoKonaniAkce
  • Liptovský Ján, Slovensko
http://linked.open...i/riv/mistoVydani
  • Praha: VŠE Praha
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 29th International Conference on Mathematical Methods in Economics 2011
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Holčapek, Michal
  • Tichý, Tomáš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Neuveden
https://schema.org/isbn
  • 978-80-7431-059-1
http://localhost/t...ganizacniJednotka
  • 17610
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