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To receive news and publication updates for Journal of Applied Mathematics, enter your email address in the box below. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Considering the uncertainty of a financial market includes two aspects: For eachthe -level set of fuzzy prices is obtained according to the fuzzy arithmetics and the definition of fuzzy-valued american put option pricing matlab. We apply a defuzzification method based on crisp possibilistic mean values of the fuzzy interest rate and fuzzy volatility to obtain the crisp possibilistic mean value of compound option price.

Finally, we present a numerical analysis to illustrate the compound option pricing under fuzzy environment. Compound options are options with other options as underlying assets. Since Geske [ 1 ] derived the closed form pricing formula using the method of partial differential equations for the first time, some scholars have extended the pricing model and proposed some new pricing methods. For example, [ 2 ] used the martingale approach and the expectation of a truncated bivariate normal variables to prove the pricing formula for 2-fold compound options, respectively.

The work of [ 3 ] extended the Geske model to a multivariate normal integral for the valuation of a compound real option. The work of [ 4 — 6 ] extended the Geske model to n-fold compound options. The work of [ 78 ] introduced time dependent volatility american put option pricing matlab an interest rate to the pricing model of compound options.

Fouque and Han [ 9 ] proposed perturbation approximation to compute the prices of compound options. There are few literature studied compound option pricing under jump-diffusion model, stochastic volatility model, or stochastic interest rate model, such as [ 10 — 12 ].

Compound option is widely employed in the field of financial derivatives pricing, for instance, American put option [ 13 ], sequential exchange options [ 14 ], and sequential American exchange property options [ 15 ].

Compound option is also widely used american put option pricing matlab the real options; examples include project valuation of new drug application [ 16 ], valuation of multistage BOT projects [ 17 ], and decision-making in petroleum exploration [ 18 ]. The literature mentioned above studied compound option within stochastic framework.

The uncertainty of the financial market includes two aspects: In the real financial market, due to market fluctuations and human errors, some parameters such as the interest rate and volatility sometimes cannot be recorded or collected precisely. The risk uncertainty could be modeled by the probability theory; the vagueness could be modeled by a fuzzy methodology, the fuzzy sets theory provides an appropriate tool for tackling this kind of uncertainty.

Therefore, the fuzzy sets theory proposed by Zadeh [ 19 ] has been widely used in the option pricing recently. The existing literature on option pricing under the fuzzy stochastic model mainly studied the European option, based on the Black-Scholes model.

For example, Yoshida [ 20 ] introduced fuzzy logic to the stochastic financial model and discussed the valuation of the European options with the uncertainty of both randomness and fuzziness. Wu [ 21 ] considered the fuzzy pattern of the Black-Scholes formula by fuzzing the interest rate, volatility, and stock price in his paper when the arithmetics in the Black-Scholes formula are replaced by fuzzy arithmetic. The work of [ 2223 ] presented a sensitivity analysis based on the Black-Scholes formula.

The work of [ 24 ] introduced a crisp weighted possibilistic mean value Black-Scholes option pricing formula. There are only few papers that studied American options or exotic options pricing within the Black-Scholes framework, such as [ 25 — 27 ], and few papers for alternative models with jumps [ 28 — 30 ].

As far as we know, there is no literature research on compound option pricing under fuzzy environment; this paper will consider both the risk and vagueness to study compound option pricing. The main contribution of this paper is that we present the -level set of fuzzy prices for each and give a sensitivity analysis of the crisp possibilistic mean value of compound option price with respect to the core value of fuzzy interest rate and fuzzy volatility.

The remainder of the paper is organized as follows. In American put option pricing matlab 2the notions of fuzzy numbers and the arithmetics of fuzzy numbers are introduced. In Section 3the pricing formula for compound option under stochastic model is introduced. Section 4 presents the fuzzy price, -level set of fuzzy prices, and the crisp possibilistic mean value of compound option price.

In Section 5a numerical analysis is performed. Finally, the conclusions are stated in Section 6. In this section we follow the notations and concepts introduced in Wu [ 2131 ]. Let be the set of all real numbers. Then a fuzzy subset of is defined by its membership function. We denote by the -level set of for all. The 0-level set of is defined by the closure of the set. Let be a real-valued function defined on.

Then is said to be upper semicontinuous if is a closed set for each. Let be a fuzzy subset of. Then is called a fuzzy number if the following conditions are satisfied: If is a fuzzy number, then the -level set is a closed interval and the -level set of is denoted by. Let and be two fuzzy numbers. Thenand are also fuzzy numbers and their -level sets are for all. If the -level set of does not contain zero, then is also a fuzzy number, and its -level set is for all.

We see that the real numbers are the special case of the fuzzy numbers when the real numbers are regarded as the crisp numbers. Let be a continuous real-valued function defined on and let be fuzzy numbers. Let be a fuzzy-valued function american put option pricing matlab by via the extension principle. Suppose that each is a compact subset of for in the range of. Then is a fuzzy number and its american put option pricing matlab set is.

The membership function of a triangular fuzzy number is defined by which is denoted byis called the core value ofand and are called the left and right spreads ofrespectively. A compound option is an option on an option. Therefore, a compound option has two expiration dates and two strike prices. There are four types of European compound options; in this paper we take a call on american put option pricing matlab call as an example.

If an investor buys a compound option at time 0, then, on the first expiration datethe option holder has the right to buy a new call option with the american put option pricing matlab price.

American put option pricing matlab new option gives the holder the right to buy the underlying asset with the strike price at time. We suppose the underlying asset price satisfies the following stochastic differential equation: The well-known closed form compound option pricing formula is presented in Geske [ 1 ] as the following lemma.

The closed form pricing formula for compound option at time 0 is where is the risk-free interest rate, is the underlying asset price at time 0, is the standard normal distribution function, is the bivariate standard normal distribution function with and as upper limits and as the correlation coefficient between the two variables, and is the unique solution of the equation where.

As discussed before, owing to the imprecise information and the fluctuations of the financial market from time to time, it is unreasonable to assume that the interest rate and the volatility are constants.

According to Wu [ 21 ] and Nowak and Romaniuk [ 29 ], we replace and in 8 by fuzzy numbers andrespectively, and replace the arithmetics by fuzzy arithmetics to get the analytical formula for the compound option under fuzzy environment, which is given in Theorem 4.

Let the interest rate and the volatility be fuzzy numbers. Then the fuzzy price of compound option is american put option pricing matlab is the unique solution of the equation where and are the left-end point and right-end point of the -level set ofrespectively, and and are the left-end point and right-end point of the american put option pricing matlab set ofrespectively.

It is easily obtained from Lemma 3 by replacing and by the fuzzy numbers andrespectively, and replacing the arithmetics by fuzzy arithmetics. Under fuzzy environment, the option price is a fuzzy number, the -level set of may be denoted asand and can be calculated as the following theorem. The american put option pricing matlab point and right-end point of the -level set can be calculated as follows: Because the standard normal distribution function and the bivariate standard normal distribution function are increasing functions, is a decreasing function; according to Lemmas 1 and 2from 12 the left-end point of the -level set is where,and are given as Theorem 5.

In the same way, can be proved. We provide a method to find a crisp number that synthesizes the fuzzy number by replacing and in Theorem 4 by their crisp possibilistic mean values andrespectively, to get the crisp possibilistic mean value of compound option price. The crisp possibilistic mean value of the compound option price is where. From Theorem 4 we have american put option pricing matlab, are given as Theorem 6. In this section, we provide some numerical results of compound option pricing under fuzzy environment.

For comparison, Table 1 presents the numerical results of the critical value and compound option price under the Black-Scholes model, where the critical value can be obtained by solving 10 using the bisection method, and the precision is. All the computations are performed using Matlab software. Triangular fuzzy numbers are applied to denote american put option pricing matlab fuzzy parameters and because of their simple american put option pricing matlab.

We takewhere is the core value of and the left and right spreads of are 0. Table 2 presents the critical value and the crisp possibilistic mean value of compound option price under fuzzy environment, where the critical value can be obtained by solving 14 using the bisection method, and the precision is.

For each match group the values of parameters, and in Table 2 are the same as those in Table 1and the cores and are equal to and in Table 1respectively. From Tables 1 and 2the compound option prices derived from the Black-Scholes american put option pricing matlab are slightly lower than the prices derived from the crisp possibilistic mean value with the same parameters. This seems to be consistent with our intuition that the crisp possibilistic mean value model contains more uncertainty than the Black-Scholes model see [ 2830 ].

But this intuition is not necessarily true, which one is bigger between and is related to the selected parameters. Similarly, from Tables 1 and 2american put option pricing matlab notice that is slightly higher than ; this conclusion is not surely true.

For example, when, american put option pricing matlab,andthen the computing result is,and ; obviously, and. In order to perform the sensitivity analysis we set our base case with,, and. Figures 1 to 7 give the sensitivity analysis of and with respect to,, andrespectively. We can see that does not vary with the change ofbecause 14 does not contain. We can see from Figures 1 to 7 that, and increase the value of increases, and and increase the value of decreases.

This is consistent with [ 17 ] that the compound option price is an increasing function with respect to, and and is a decreasing function with respect to and. Table 3 presents the closed interval of -level set of calculated from Theorem 5 with,, and. From Table 3forit means that the option price will lie in the closed interval with belief degree 0. This interval can provide reference for financial investors. That is to say, if a financial investor is satisfied with belief degree 0.

In addition, the financial investor may think the price is overvalued if the actual price option is higher than We also notice that the length of the interval tends to become smaller with the increasing of belief degree and the interval with smaller belief degree contains the interval with larger belief degree.

American put option pricing matlab the uncertainty of the financial market includes both risk and vagueness; the compound option price will turn into a fuzzy number. For any american put option pricing matlab degree, the closed interval of price is obtained according to the fuzzy arithmetics and the definition of fuzzy-valued function; this american put option pricing matlab the financial investor to make more flexible investment decision.

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Using the Finite Volume Discretization Method, we derive the equations required for an efficient implementation in Matlab. The implicit Euler time-stepping of the solver guarantees a stable behavior and convergence. Now, we write a program which implements the PDE solver. Furthermore, we use a start mesh with. Rearranging equation 2 , we find that matrix is a tri-diagonal matrix with the following properties:.

Now, the solver according to equation 3 is simple. It solves for at each time step:. And we can zoom in on , the strike price of the option:. We can analyze the hedge parameter delta, , the first derivative of w.

We can analyze the hedge parameter gamma, , the second derivative of w. This solver uses a finite volume discretization and an implicit Euler time-stepping.

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