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In this paper we propose a binomial option pricing model to price a call option using O(2, 2)−trapezoidal type fuzzy numbers for the discrete Cox-Ross-Rubinstein binomial risk neutral option pricing model. The approach developed in the paper is illustrated with the help of a numerical example.

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authors consider the case of option pricing for a binomial process—the first in a series of articles in Financial Engineering.

by the entire credit spread term structure. The paper provides the defaultable bond and credit default swap option price in a probability setting equipped with a subfiltration structure. The Euler-Maruyama stochastic integral approximation and the Monte Carlo method are applied to develop a numerical algorithm

In this paper, we empirically compare the pricing and forecasting per-formance of the wavelet option pricing model, the spline method, and the parametric stochastic volatility model with jumps. Both in-sample pric-ing and out-of-sample forecasting accuracy are examined using the US and UK index

*Very preliminary. Please do not quote. Comments are welcome. Valuing Common Stock using an Option Pricing Model It is well known that common stock, due to its limited liability feature, may be viewed as a call option on the firm value. Several authors have suggested that option pricing models (OPM

The Black-Scholes (1973) option pricing model provides the foundation for the modern theory of options valuation. In actual applications, how-ever, the model has certain well-known deficiencies. For example, when calibrated to accurately price at-the-money options the Black-Scholes

The valuation of an option is importance topic in finance. The classical option pricing models depend on the stochastic calculus to obtain some results. In practice, there are some fuzzy and imprecise factors in stock market. Fuzzy random theory may be an efficient tool to tackle with uncertainty

The problem of determining the European-style option price in the incomplete market has been examined within the framework of stochastic optimization. An analytic method based on the discrete dynamic programming equation (Bellman equation) has been developed that gives the general formalism