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  • During last decades the stochastic simulation approach, both via MC and 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 – depend 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 option price modeling in terms of fuzzy-random variables. In particular, we suggest three distinct fuzzy-random processes as an alternative to a standard crisp model. Application possibilities are shown on illustrative examples.
  • During last decades the stochastic simulation approach, both via MC and 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 – depend 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 option price modeling in terms of fuzzy-random variables. In particular, we suggest three distinct fuzzy-random processes as an alternative to a standard crisp model. Application possibilities are shown on illustrative examples. (en)
Title
  • Imprecise input data and option valuation problem
  • Imprecise input data and option valuation problem (en)
skos:prefLabel
  • Imprecise input data and option valuation problem
  • Imprecise input data and option valuation problem (en)
skos:notation
  • RIV/61989100:27510/13:86086859!RIV14-GA0-27510___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(EE2.3.20.0296), P(GA13-13142S), S
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
  • 79266
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27510/13:86086859
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • fuzzy random variable, fuzzy quantity, fuzzy stochastic process, fuzzy distribution function (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9D9FA2E6ED58]
http://linked.open...v/mistoKonaniAkce
  • Jihlava
http://linked.open...i/riv/mistoVydani
  • Jihlava
http://linked.open...i/riv/nazevZdroje
  • Mathematical Methods in Economics 2013 : 31st international conference : 11-13 September 2013, Jihlava, Czech Republic
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Holčapek, Michal
  • Tichý, Tomáš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • College of Polytechnics Jihlava
https://schema.org/isbn
  • 978-80-87035-76-4
http://localhost/t...ganizacniJednotka
  • 27510
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