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  • The option pricing model performance crucially depends on the ability to estimate all necessary input parameters successfully. Within the standard models of Black-Scholes type, the most important parameter is volatility. Since it is often very difficult to obtain a single number, an alternative can be to apply interval approach or more generalized fuzzy-random approach. In this paper recent knowledge of fuzzy numbers and their approximations is utilized in order to suggest fuzzy-random simulation approach to option price modeling, ie. we use fuzzy-random variables. In particular, we suggest to replace a crisp volatility parameter in the standard market model (ie. Black-Scholes type) by a fuzzy random variable, which can be easily evaluated by Monte Carlo simulation. Application possibilities are shown on illustrative examples. In particular, we evaluate the model for various input data and option types. The results are compared to the Black-Scholes option price and market option prices.
  • The option pricing model performance crucially depends on the ability to estimate all necessary input parameters successfully. Within the standard models of Black-Scholes type, the most important parameter is volatility. Since it is often very difficult to obtain a single number, an alternative can be to apply interval approach or more generalized fuzzy-random approach. In this paper recent knowledge of fuzzy numbers and their approximations is utilized in order to suggest fuzzy-random simulation approach to option price modeling, ie. we use fuzzy-random variables. In particular, we suggest to replace a crisp volatility parameter in the standard market model (ie. Black-Scholes type) by a fuzzy random variable, which can be easily evaluated by Monte Carlo simulation. Application possibilities are shown on illustrative examples. In particular, we evaluate the model for various input data and option types. The results are compared to the Black-Scholes option price and market option prices. (en)
Title
  • The Case of Imprecisely Stated Volatility: A Fuzzy-Random Approach to Option Pricing
  • The Case of Imprecisely Stated Volatility: A Fuzzy-Random Approach to Option Pricing (en)
skos:prefLabel
  • The Case of Imprecisely Stated Volatility: A Fuzzy-Random Approach to Option Pricing
  • The Case of Imprecisely Stated Volatility: A Fuzzy-Random Approach to Option Pricing (en)
skos:notation
  • RIV/61989100:27510/12:86083106!RIV13-MSM-27510___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 126050
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  • RIV/61989100:27510/12:86083106
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  • Monte Carlo simulation; plain vanilla option; fuzzy random variable (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [84BBD17513E8]
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  • Ostrava
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  • Karviná
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  • Proceedings of the 13th International Conference on Finance and Banking : 12-13 October 2011, Ostrava, Czech Republic
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Michal, Holčapek
  • Tichý, Tomáš
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000309369700013
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Silesian University, School of Business Administration
https://schema.org/isbn
  • 978-80-7248-753-0
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  • 27510
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