. "Soft Binomial American Option Pricing Model (fuzzy - stochastic approach)" . . . "Dublin, Irsko" . "Soft Binomial American Option Pricing Model (fuzzy - stochastic approach)"@en . . "[EBFDE9ED4545]" . . . . . . "RIV/61989100:27510/05:00011860" . . . "543335" . . . "Dluho\u0161ov\u00E1, Dana" . "2"^^ . "12"^^ . . "2"^^ . "Soft binomick\u00FD model oce\u0148ov\u00E1n\u00ED americk\u00FDch opc\u00ED (fuzzy-stochastick\u00FD p\u0159\u00EDstup)"@cs . "Dublin" . "The real option valuation is relatively new concept in financial decision-making. There are usually two basic aspects that are studied: contingent claim features (payoff functions) and risk (stochastic process of underlying assets). The stochastic discrete binomial models and continuous Black-Scholes-Meton models are usually applied. However, there is not in several situations in real option methodology application to have at to disposal input data of required quality. Two aspects of input data uncertainty should be distinguished; risk (stochastic) and vagueness (fuzzy). Traditionally, input data are in a form of real (crisp) numbers or crisp-stochastic distribution function. However, in several cases, input data is possible introduce only vaguely, by fuzzy numbers. Therefore, hybrid models, combination of risk and vagueness could be useful approach in real option valuation. Hybrid fuzzy-stochastic binomial model under fuzzy numbers (T-numbers) and Decomposition principle is proposed and described. In"@en . "Soft Binomial American Option Pricing Model (fuzzy - stochastic approach)"@en . "Soft Binomial American Option Pricing Model (fuzzy - stochastic approach)" . "2005-06-27+02:00"^^ . "Real option; Call option; Discrete Binomial Model; Black-Scholes-Merton model; Decision support system; Finance; Fuzzy sets; Pricing; Stochastic processes"@en . "RIV/61989100:27510/05:00011860!RIV06-GA0-27510___" . . . . . . "0-9768149-5-1" . . "The real option valuation is relatively new concept in financial decision-making. There are usually two basic aspects that are studied: contingent claim features (payoff functions) and risk (stochastic process of underlying assets). The stochastic discrete binomial models and continuous Black-Scholes-Meton models are usually applied. However, there is not in several situations in real option methodology application to have at to disposal input data of required quality. Two aspects of input data uncertainty should be distinguished; risk (stochastic) and vagueness (fuzzy). Traditionally, input data are in a form of real (crisp) numbers or crisp-stochastic distribution function. However, in several cases, input data is possible introduce only vaguely, by fuzzy numbers. Therefore, hybrid models, combination of risk and vagueness could be useful approach in real option valuation. Hybrid fuzzy-stochastic binomial model under fuzzy numbers (T-numbers) and Decomposition principle is proposed and described. In" . . "P(GA402/04/1357)" . "1-12" . "Soft binomick\u00FD model oce\u0148ov\u00E1n\u00ED americk\u00FDch opc\u00ED (fuzzy-stochastick\u00FD p\u0159\u00EDstup)"@cs . "Global Finance Conference 2005" . "Ocen\u011Bn\u00ED re\u00E1ln\u00FDch opc\u00ED je relativn\u011B nov\u00FD koncept ve finan\u010Dn\u00EDm rozhodov\u00E1n\u00ED. Obvykle jsou dva aspekty, kter\u00E9 jsou studov\u00E1ny: podm\u00EDn\u011Bn\u00E9 n\u00E1roky (v\u00FDplatn\u00ED funkce) a riziko (stochastick\u00FD proces podkladov\u00E9ho aktiva). Obvykle jsou po\u017Eadov\u00E1ny stochastick\u00E9 nespojit\u00E9 binomick\u00E9 modely a pokra\u010Duj\u00EDc\u00ED Black-Scholesovy modely. Nicm\u00E9n\u011B, pro aplikaci metodologie re\u00E1ln\u00FDch opc\u00ED nen\u00ED mnoho situac\u00ED, kdy m\u00E1me k dispozici vstupn\u00ED data po\u017Eadovan\u00E9 kvality. Dva aspekty vstupn\u00EDch dat nejistoty by m\u011Bly b\u00FDt v\u00FDznamn\u00E9: riziko (stochastick\u00E9) a neur\u010Ditost (fuzzy). Standardn\u00ED vstupn\u00ED data jsou v podob\u011B re\u00E1ln\u00FDch \u010D\u00EDsel nebo crisp-stochastick\u00E9 distribu\u010Dn\u00ED funkce. Nicm\u00E9n\u011B, v n\u011Bkter\u00FDch p\u0159\u00EDpadech je mo\u017En\u00E9 vlo\u017Eit vstupn\u00ED data jenom neur\u010Dit\u011B, pomoc\u00ED fuzzy \u010D\u00EDsel. Z tohoto d\u016Fvodu, hybridn\u00ED modely, kombinuj\u00EDc\u00ED riziko a nejistotu, mohou b\u00FDt u\u017Eite\u010Dn\u00FDm p\u0159\u00EDstupem v ocen\u011Bn\u00ED re\u00E1ln\u00FDch opc\u00ED. Je pops\u00E1n hybridn\u00ED fuzzy-stochastick\u00FD binomick\u00FD model s fuzzy \u010D\u00EDsly (T-\u010D\u00EDsly) a dekompozice princip\u016F. Vstupn\u00ED data jsou v podob\u011B fuzzy \u010D\u00EDsel a v\u00FDsledek, mo\u017En\u00E1 o\u010Dek\u00E1"@cs . . "27510" . "Trinity college" . "Zme\u0161kal, Zden\u011Bk" . .