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Abstract and Applied Analysis
Volume 2013, Article ID 194286, 10 pages
http://dx.doi.org/10.1155/2013/194286
Research Article

Solution of the Fractional Black-Scholes Option Pricing Model by Finite Difference Method

Center for Econometric Analysis and Forecasting, School of Mathematics and Quantitative Economics, Dongbei University of Finance and Economics, Dalian 116025, China

Received 17 March 2013; Revised 2 June 2013; Accepted 16 June 2013

Academic Editor: Changbum Chun

Copyright © 2013 Lina Song and Weiguo Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This work deals with the put option pricing problems based on the time-fractional Black-Scholes equation, where the fractional derivative is a so-called modified Riemann-Liouville fractional derivative. With the aid of symbolic calculation software, European and American put option pricing models that combine the time-fractional Black-Scholes equation with the conditions satisfied by the standard put options are numerically solved using the implicit scheme of the finite difference method.

1. Introduction

The theory and methodology of partial differential equation started to become popular to study option pricing problems, after the classical Black-Scholes equation was proposed. The available research results include mainly two aspects: one is to give values of options using more powerful numerical and analytic methods; the other is to derive new pricing models that reflect the actual financial market more closely.

The Black-Scholes equation has been increasingly attracting interest over the last two decades since it provides effectively the values of options. But the classical Black-Scholes equation was established under some strict assumptions. Therefore, some improved models have been proposed to weaken these assumptions, such as stochastic interest model , Jump-diffusion model , stochastic volatility model , and models with transactions costs [4, 5]. With the discovery of the fractal structure for financial market, the fractional Black-Scholes models  are derived by replacing the standard Brownian motion involved in the classical model with fractional Brownian motion. These fractional Black-Scholes models are still partial differential equations with integer order derivative, which are reduced to the classical Black-Scholes equation if we let the Hurst exponent . The differential equation involving derivatives of fractional order is a powerful tool for studying fractal geometry and fractal dynamics. As a generalization of the integer-order differential equation, fractional differential equation is used to model important phenomena in various fields such as fluid flow, electromagnetic, acoustics, electrochemistry, cosmology, and material science. Recently, fractional partial differential equation was introduced more and more into financial theory. Wyss  gave the fractional Black-Scholes equation with a time-fractional derivative to price European call option. Cartea and del-Castillo-Negrete  gave several fractional diffusion models of option prices in markets with jumps and priced barrier option using fractional partial differential equation. Jumarie [12, 13] derived the time- and space-fractional Black-Scholes equations and gave optimal fractional Merton's portfolio. The aim of this paper is to try to combine Jumarie's time-fractional Black-Scholes equation with the terminal and boundary conditions satisfied by the standard put options to study the pricing problems for European and American put options.

In the present work, the option price is suggested to be subject to the time-fractional Black-Scholes equation [12, 13] with the following form: where denote risk-free interest rate and volatility, respectively. involved in (1) is a modified Riemann-Liouville fractional derivative , which is defined using the equality and can be represented as

The value of European put option is taken as a solution of (1) with the following terminal and boundary conditions:

Pricing European put option based on (1) and (4) can be implemented by finite differences approximation. For American put option, this idea is operable. The main problem for pricing an American put option is to consider the possibility of early exercise. To avoid arbitrage, the option value at each point in the space cannot be less than the intrinsic value . For that, the principle of dynamic programming (at a given time, the optimal strategy corresponds to the maximum of either the exercise value or the value associated with selecting an optimal strategy an instant later)  expressed by the following equation is applied, which can be implemented using finite difference method and successive overrelaxation (SOR) method

2. Numerical Scheme

Numerical and analytical methods  are used to study a great quantity of differential equations. Among them, the finite difference method is a direct and effective numerical algorithm. With this technique, the differential equation is transformed into a difference equation by the discretization of derivatives, and numerical solutions are finally obtained. The approach has been extended successfully to deal with various fractional differential equation [1822, 3843]. In this section, the implicit finite difference mode is given.

Take the change of variable Equation (1) can be transformed into the following form:

In order to use finite difference approximation, we start by and . Let , () be the grid sizes in space and time; the computational domain is discretized by a uniform grid with () and (), where is a realistic and practical approximation to infinity. denotes an approximate solution of (7) in at the time .

As pointed out in [12, 13], Jumarie's definition and the so-called Caputo's definition yield the same result when the function is differentiable. So, we take Caputo finite difference approximation  for the modified Riemann-Liouville fractional derivative involved in (7), namely, where and that satisfies (i) , (ii) [22, 38].

For spatial derivative, we use the following difference approximation:

Substituting (8) and (9) to (7) can derive or where the local truncation error is .

From (11), we get the following equality when : After combining like terms, we can derive the following equation: For , we obtain

If the minimum limit of variable is , the corresponding implicit difference approximation can be derived by replacing of the above results (13) and (14) with .

From the terminal and boundary conditions of the European put option, we can get

In the case of American put option, we perform the above procedure. At the same time, we should check for the possibility of early exercise after computing and set Under this implicit scheme, we do not directly apply (16) at each step but use SOR method to complete this procedure, which was suggested in .

3. Stability and Convergence

In this section, we will analyze the stability and convergence of implicit finite difference scheme (13) and (14) using Fourier analysis involved in . For that, (13) and (14) are rewritten as where

3.1. Stability Analysis

If is another approximate solution of (13) and (14), we define the round-off error which satisfies the following equations according to (17):

Now, we introduce the grid function in :

Recall the result of ; defining can get where .

The solution of (19) and (20) is supposed to have the following form: where , .

Substituting the above expression into (19), we get Simplifying (25), we obtain where   .

Proposition 1. If is a solution of (26), then .

Proof. For , the first equality of (26) gives . If then using the second equality of (26), we obtain

According to (13) and (14), we can obtain the following conclusion using Proposition 1 and equality (23).

Theorem 2. The difference scheme (13) and (14) is unconditionally stable.

3.2. Convergence Analysis

Suppose that is an exact solution of (7) at grid point and is the difference solution of (13) and (14); we define the error which satisfies the following equation according to (17): where () and is a positive constant.

For completing the proof of convergence, we recall several results below which came from .

Similar to the stability analysis,  constructed the following grid function:

Defining with the norms can obtain where

We can let where , .

Substituting the above expressions into (29) and simplifying them can get where   .

Proposition 3. There exists a positive constant , so that .

Proof. From , we know Then the first equality of (37) tells us that If we let using the second equality of (37), we can obtain

According to equalities (34) and inequality (38), we can obtain the following conclusion using Proposition 3.

Theorem 4. The implicit difference scheme (13) and (14) is -convergent.

4. Computational Examples

Option pricing model based on the time-fractional differential equation (1) is studied. The implicit difference scheme of (7) is given in Section 2. From the finite difference forms, it is clear that (13) and (14) are just the implicit difference scheme of the classical Black-Scholes equation if we let . This known implicit difference results are given in [16, 17, 36]. In this section, we focus mainly on investigating the results with . To explain the stability and convergence of the implicit numerical schemes, we firstly take Example 1 with the given terminal and boundary condition as an example.

Example 1. Consider (1) that is subject to the following conditions:
Under the condition , (1) has an analytical solution

Figure 1 shows analytical solution and numerical solution obtained by the implicit difference method at time when , , , , , , and . Numerical solution compares well with analytical solution, which proves that the implicit scheme is stable. Under the same parameters, Figure 2 gives the absolute error between numerical solutions and analytical solutions, which illustrates that the numerical results are convergent.

Figure 1: Solution curve with . Solid line: numerical solution; Soft dot: analytical solution.
Figure 2: Absolute error between numerical solution and analytical solution. dash-dotted line: ; dotted line: ; solid line: .

Example 2. European put option pricing model is based on (1) and the condition (4) under the following parameters: Through coding, the European put option values with are plotted in Figure 3 at .

Figure 3: European put option. dash-dotted line: ; dotted line: ; solid line: .

Example 3. Consider American put option pricing model. The following parameters are selected for the present study: Figure 4 indicates a price comparison of the American put option at (relaxation parameter and tolerance parameter are 1.2 and 0.001 in the applied process of SOR method).

Figure 4: American put option. dash-dotted line: ; dotted line: ; solid line: .

Using Examples 1, 2, and 3, we examine the implementation of the implicit finite difference method for the fractional partial differential equation system. According to Figures 1 and 2 in Example 1, we can confirm that the implicit numerical scheme is stable and convergent. From Figures 3 and 4, we can see that the numerical scheme is very effective. Figures 3 and 4 present numerical simulation of the price of the European and American put options when the order of the time-fractional derivative takes different values. Their visible shapes and development trend are similar to the classical put option pricing model based on the standard Black-Scholes equation, which illustrates the essential characteristics of the European and American put options. For making the figures clear, we choose the small values of and and get the similar conclusions if largening the number of steps in time and space. As a generalization of the standard models, these fractional Black-Scholes models are powerful and will be of great interest to researchers in further work.

5. Conclusions

In this work, the finite difference method is employed to solve the time-fractional Black-Scholes equation together with the conditions satisfied by the standard put options. Application of the fractional differential equation to the pricing theory of option is in its beginning stage and needs more further work. This fractional model mentioned in this paper can model the price of other financial derivatives like warrant, swaps, and so on. The successful application of the finite difference method proves that this technique is effective and requires less computational work to solve fractional partial differential equation.

Acknowledgments

The authors would like to express their gratitude to reviewers for the careful reading of the paper and for their constructive comments which greatly improve the quality of this paper. The work is partially supported by the National Natural Science Foundation of China (no. 71171035, no. 71273044, and no. 71271045).

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