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Convergence and stability of the compensated split-step θ-method for stochastic differential equations with jumps
Advances in Difference Equations volume 2014, Article number: 209 (2014)
In this paper, we develop a new compensated split-step θ (CSSθ) method for stochastic differential equations with jumps (SDEwJs). First, it is proved that the proposed method is convergent with strong order 1/2 in the mean-square sense. Then the condition of the mean-square (MS) stability of the CSSθ method is obtained. Finally, some scalar test equations are simulated to verify the results obtained from theory, and a comparison between the compensated stochastic theta (CST) method by Wang and Gan (Appl. Numer. Math. 60:877-887, 2010) and CSSθ is analyzed. Meanwhile, the results show the higher efficiency of the CSSθ method.
In this paper, we consider one-dimensional Itô stochastic differential equations (SDEs) with Poisson-driven jumps
for , with , where denotes , , , , is a scalar standard Wiener process, and is a scalar Poisson process with intensity λ.
Recently, stochastic differential equations with jumps (SDEwJs) are becoming increasingly used to model real-world phenomena in different fields, such as economics, finance, biology, and physics. However, few analytical solutions have been proposed so far; thus, it is necessary to develop numerical methods for SDEwJs and study the properties of these methods. For example, Higham and Kloeden  studied the convergence and stability of the implicit method for jump-diffusion systems, and they further analyzed the strong convergence rates of the backward Euler method for a nonlinear jump-diffusion system . Chalmers and Higham  studied the convergence and stability for the implicit simulations of SDEs with random jump magnitudes. Higham and Kloeden  constructed the split-step backward Euler (SSBE) method and the compensated split-step backward Euler (CSSBE) method for nonlinear SDEwJs. Bruti-Liberati and Platen [5, 6] developed strong and weak approximations of SDEwJs.
Lately, Wang and Gan  started to focus on the CST method for stochastic differential equations with jumps. Hu and Gan  studied the convergence and stability of the balanced methods for SDEwJs. The split-step θ (SSθ) method was firstly developed by Ding et al.  to solve the stochastic differential equations. Thus, we will construct the compensated split-step θ method (CSSθ) for SDEwJs.
In this paper, we investigate the convergence and mean-square stability of the CSSθ method for SDEwJs. The outline of the paper is as follows. In Section 2, we introduce some notations and hypotheses and give the CSSθ method for SDEwJs. In Section 3, we prove that the numerical solutions produced by the CSSθ method converge to the true solutions with strong order 1/2. In Section 4, the mean-square stability of the CSSθ method for linear test equation is studied. At last, some numerical experiments are used to verify the results obtained from the theory.
2 The compensated split-step θ-method
For the existence and uniqueness of the solution for (1.1), we usually assume that f, g, and h satisfy the following assumptions:
(H1) (The uniform Lipschitz condition) There is a constant , for all , such that
(H2) (The linear growth condition) There is a constant , for all , such that
For a constant step size , we first define the split-step θ (SSθ) method for (1.1) by and
where , is the numerical approximation of with . Moreover, the increments are independent Gaussian random variables with mean 0 and variance Δt; are independent Poisson distributed random variables with mean and variance .
If we give , the SSθ method becomes the SSBE method in . If , the SSθ method is an explicit method.
Note that the compensated Poisson process
which is a martingale. Defining
we can rewrite the jump-diffusion system (1.1) in the form
We note that also satisfies the uniform Lipschitz condition and linear growth condition with larger constants
Then we define the compensated split-step θ method (CSSθ) for (1.1) by and
If we give , the CSSθ method becomes the CSSBE method in .
To answer the question of the existence of numerical solution, we will give the following lemma.
Lemma 2.1 Assume that satisfies (2.1), and let , , then equation (2.7) can be solved uniquely for , with probability 1.
Proof Writing (2.7) as , , and using condition (2.6), we have
Then the result follows from the classical Banach contraction mapping theorem . □
3 Strong convergence on a finite time interval
In this section, we prove the strong convergence of the CSSθ method for problem (1.1) on a finite time interval , where T is a constant.
When Lemma 2.1 is followed, we find it is convenient to use continuous-time approximation solution in our strong convergence analysis. Hence, for , we can define the two step-functions:
where N is the largest number such that , and is the indicator function for the set A, i.e.,
When , Lemma 2.1 ensures the existence of by (2.7), then we define
Thus we can rewrite (3.3) in the integral form as follows:
It is easy to verify that , that is, and coincide with the discrete solutions at the gridpoints. Hence we refer to as a continuous-time extension of the discrete approximation . So our plan is to prove a strong convergence result for .
Now we begin the proof of the strong convergence of the CSSθ method, our first lemma shows the relationship between and .
Lemma 3.1 Suppose satisfies (2.2), and let , , then there exist two positive constants and such that
where and are produced by (2.7) and (2.8).
Proof Squaring both sides of (2.7), we find
Using the elementary inequality , we obtain
Due to , linear growth condition (2.6), and , we can get
Taking mathematical expectation for both sides, we can obtain
Since , thus , then by and , we have
where and . The proof is completed. □
The next lemma shows that the discrete numerical solutions and (), produced by the CSSθ method, have bounded second moments.
Lemma 3.2 Under conditions (2.1)-(2.2), let and () be produced by (2.7) and (2.8), and let , , then
where and are two positive constants independent of Δt.
Proof By Lemma 2.1, we can express the CSSθ method (2.7) and (2.8) in the following form:
Squaring both sides, taking the mathematical expectation and using the element inequality , we have
Now, using the Cauchy-Schwarz inequality and the inequality , the linear growth condition (2.6) and Fubini’s theorem, we can get
Using the martingale isometry and linear growth condition (2.2), we have
For the jump integral, as the compensated Poisson process is a martingale, so we use the isometry
(see, for example, ), then we have
Inserting (3.13)-(3.15) in (3.12) gives
By Lemma 3.1, we can derive that
are both independent of Δt.
Then, using the discrete Gronwall inequality, we can get
Then, by Lemma 3.1, we can obtain that
The next lemma shows that the continuous-time approximation in (3.4) remains close to the step functions and in the mean square sense.
Lemma 3.3 Under conditions (2.1)-(2.2), let and be produced by (2.7) and (2.8), and let , , then there exist two positive constants and that are independent of Δt, such that
where , , , and are defined by (3.1), (3.2), (3.4), respectively.
Proof For any , there exists a nonnegative integer n such that
Squaring both sides and using the element inequality , we have
Taking mathematical expectation, by the element inequality , and using the martingale isometry, we have
By the linear growth conditions (2.2) and (2.6), we get
Since and on , we have
Then, for each , and by Lemma 3.2, we can derive
where . Thus we can prove (3.18).
Now we give the proof of (3.19).
By (2.7) and for each , we get
Using the inequality , and , we can get
Taking mathematical expectation, and by the linear growth condition (2.6),
Then by Lemma 3.2 we can derive
Then, by the element inequality and using (3.20) and (3.21), we have
where . Then we have proved (3.19). □
Now we use the above lemmas to prove a strong convergence result.
Definition 3.1 A numerical method is said to have strong order of convergence equal to γ if there exists a constant C such that the numerical solution sequence produced by this numerical scheme satisfies
for any fixed , and Δt sufficiently small.
Theorem 3.1 Under conditions (2.1)-(2.2), let , , the continuous-time approximate solution defined by (3.4) will converge to the true solution of (2.5) in the mean square sense, i.e.,
where is a positive constant independent of Δt.
Proof From (2.5) and (3.4), we have
For any , using the Cauchy-Schwarz inequality and the inequality , we have
Now using the Doob martingale inequality for the two martingale terms, we have
Then Fubini’s theorem and the martingale isometries give
Applying Lipschitz conditions (2.1) and (2.6), we get
Finally, applying Lemma 3.3, we have
Using the Gronwall inequality (see ), we have
Thus for any , we have
4 Mean-square stability
In order to study the stability property of the CSSθ method, we consider a linear test equation with scalar coefficients
Applying the CSSθ method (2.7)-(2.8) to equation (4.1), we have
Definition 4.1 Under condition (4.2), a numerical method applied to equation (4.1) is said to be MS-stable if there exists such that the numerical solution sequence produced by this numerical scheme satisfies
for all .
Theorem 4.1 Under condition (4.2), then for
the CSSθ method (2.7)-(2.8) applied to equation (4.1) is MS-stable.
Proof Assuming that , from (4.3) we have
Substituting this into (4.4) yields
Squaring both sides of (4.8), we can get
Noting that , , , , we have
By the iteration of (4.10), we conclude that if
which is equivalent to
If , (4.13) becomes
By (4.2), we know that (4.15) holds for all , i.e., the CSSθ method is MS-stable for all . Note that if , the CSSθ method reduces to CSSBE, and (4.15) coincides with Theorem 7 which was studied in .
If , let
In view of (4.2), we know that , then (if , , i.e., , , then equation (4.1) becomes nonsense), so we can get
So has two real roots and , with , where
So we can easily obtain that holds when
From (4.13), we know that the CSSθ method is MS-stable. This proves the theorem. □
5 Numerical experiments
We consider the following equation:
Equation (5.1) has the exact solution
see, for example, .
To illustrate the convergence order and the linear mean-square stability of the CSSθ method, we choose the following examples from the reference .
Example 5.1 , , , .
Example 5.2 , , , .
In this section, the data used in all figures are obtained by the mean square of data by 1,000 trajectories, that is, , ; in all figures denotes the mesh-point.
To show the strong convergence order of the CSSθ method, we apply the CSSθ method to Example 5.1. First, we plot the exact solution of Example 5.1 for one sample path and the CSSθ approximations in Figure 1. Then we simulate the numerical solutions with five different step sizes for , . The mean-square errors all measured at time are estimated by trajectory averaging. We plot our approximation to against Δt on a log-log scale. For reference a dashed line of slope one-half is added. We see that the slopes of the two curves appear to match well in Figure 2. Hence, our results are consistent with a strong order of convergence equal to 1/2.
To illustrate the step size h on the mean-square stability of the CSSθ method, we applied the CSSθ method to Examples 5.1 and 5.2.
For Example 5.1, we first choose , then by Theorem 4.1 we know that the CSSθ method is MS-stable when . Figure 3 illustrates the numerical solution produced by the CSSθ method is MS-stable when . However, applied to the same test equation, and also choose , then by Theorem 3.1 in  the CSTM is MS-stable when the step size .
When we choose , by Theorem 4.1 we know that the CSSθ method is MS-stable when , while the CST method in  is MS-stable when the step size . Figure 4 illustrates the numerical solution produced by the CSSθ method is MS-stable when . At the same times we know that the Euler-Maruyama (EM) method in  is MS-stable for Example 5.1 when the step size .
For Example 5.2, we note that , then the theta method in  is not guaranteed to preserve stability for all . However, if we choose , then by Theorem 4.1 we know that the CSSθ method is MS-stable when , and when , . Figure 5 and Figure 6 (upper) illustrate the numerical solution produced by the CSSθ method is MS-stable for Example 5.2 when the step size .
At last, Figure 6 (lower) shows that the numerical solution of the CSSθ method is still stable when . This implies that maybe the mean-square stability bound we obtained by Theorem 4.1 is not optimal.
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This research was supported with funds provided by the National Natural Science Foundation of China (Nos. 11226321, 11272229 and 11102132). We thank two anonymous reviewers for their very valuable comments and helpful suggestions which improved this paper significantly.
The authors declare that they have no competing interests.
All the authors contributed equally to this work. They all read and approved the final version of the manuscript.
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Tan, J., Mu, Z. & Guo, Y. Convergence and stability of the compensated split-step θ-method for stochastic differential equations with jumps. Adv Differ Equ 2014, 209 (2014). https://doi.org/10.1186/1687-1847-2014-209
- stochastic differential equations
- Poisson jumps
- compensated split-step θ-method
- mean-square stability