The Existence of Strong Solutions for a Class of Stochastic Differential Equations

Abstract

In this paper, we will consider the existence of a strong solution for stochastic differential equations with discontinuous drift coefficients. More precisely, we study a class of stochastic differential equations when the drift coefficients are an increasing function instead of Lipschitz continuous or continuous. The main tools of this paper are the lower solutions and upper solutions of stochastic differential equations.

1. Introduction

There are many works [1–3] about the existence and uniqueness of strong or weak solutions for the following stochastic differential equation (denoted briefly by SDE): where and are called drift and diffusion coefficients, respectively. is standard Brownian motion. Usually, the drift and diffusion coefficients are Lipschitz or local Lipschitz continuous or at least are continuous with respect to when the existence and uniqueness of solutions are investigated. In fact, the solutions of stochastic differential equations may exist when their drift and diffusion coefficients are discontinuous with respect to . Therefore, many authors discussed the existence of solutions for SDE with discontinuous coefficients. For example, L. Karatzas and S. E. Shreve [1] (Proposition 3.6 of §5.3) considered the existence of a weak solution when the drift coefficient of SDE need not be continuous with respect to . A. K. Zvonkin [4] considered the following stochastic differential equation with a discontinuous diffusion coefficient: where The weak solution of this stochastic differential equation exists, but there is not the strong solution. N. V. Ktylov [5] and N. V. Ktylov and R. Liptser [6] also discussed existence issues of SDE when their diffusion coefficients are discontinuous with respect to . And many authors also considered the approximation solutions of SDE with discontinuous coefficients, such as [7–11].

In this paper, we will consider the existence of a strong solution of SDE (1) when the drift coefficient is an increasing function but need not be continuous with respect to and the diffusion coefficient satisfies condition. Section 1 is an introduction. In Section 2, we will show a comparison theorem by using the upper and lower solutions of SDE. We will prove our main result by using the above comparison theorem in Section 3.

2. The Setup and a Comparison Theorem

In our paper, we just consider a 1-dimensional case. We always assume that is a completed probability space, is a real-valued Brownian motion defined on , and is natural filtration generated by the Brownian motion ; i.e., for any

We consider SDE (1) with coefficients and , where and are a positive real number and real number, respectively. And we use to denote norm of . The following is the definition of a strong solution for SDE.

Definition 1. An adapted continuous process defined on is said to be a strong solution for SDE (1) if it satisfies thatholds with probability 1.
Moreover, and are two strong solutions of SDE (1); then . Under this condition, the solution of SDE (1) is said to be unique.

The following is the conception of upper and lower solutions for stochastic differential equations, which are given by N. Halidias and P. E. Kloeden [12]. Many authors discussed the upper and lower solutions of the stochastic differential equation by using the other name which is the solutions of the stochastic differential inequality, for example, S. Assing and R. Manthey [13] and X. Ding and R. Wu [14].

Definition 2. An adapted continuous stochastic process (resp., ) is an upper (resp., lower) solution of SDE (1) if the inequalities
(1) ;
(2) ,
hold with probability 1.

Remark 3. It is not an easy thing to calculate the exact upper and lower solution of the general stochastic differential equations. However, one can discuss the existence of upper and lower solutions. S. Assing and R. Manthey [13] discussed the “maximal/minimal solution” of the stochastic differential inequality. They proved the existence of a “maximal/minimal solution” under some conditions. However, it is easy to show there exist the upper solutions of stochastic differential equations if the minimal solution of the stochastic differential inequality exists. In fact, the minimal solution is special upper solutions of stochastic differential equations. Similarly, we can show the existence of the lower solution by using the maximal solution of the stochastic differential inequality.

Usually, the existence and uniqueness of solutions of SDE (1) are investigated under the conditions in which the diffusion coefficient satisfies Lipschitz condition and liner growth condition. In fact, the Lipschitz condition can be generalized. In this paper, the diffusion coefficient satisfies the () condition.

(): For , there exist an increasing function and a predictable process such thatfor all , and with .

Note that the Lipschitz condition satisfies the () condition. The following lemma is an important tool of this paper and had to be proved in proposition 2.3 of X. Ding and R. Wu [14].

Lemma 4. In SDE (1), we assume satisfies and satisfies that, for each , there exists a measurable process such that for all and with . Then SDE (1) has a unique local (explosion in the finite time) strong solution.

Remark 5. Moreover, if and satisfy the liner growth condition (cf. J. Jacod and J. Memin [15]) where , is a predictable process such that Then SDE (1) has a unique global strong solution.

The following theorem can be considered as a comparison theorem, and we will use it to arrive at our main result.

Theorem 6. Let be predictable such that , and let be predictable. Suppose that satisfies and there exists a predictable process such that where And suppose that and are upper and lower solutions of the following SDE:such that
Then there is a unique strong solution which satisfies that for any holds with probability 1.

Proof. Obviously, we have that SDE (10) has a unique strong solution by using Lemma 4 and Remark 5. In the following we will show We only prove , because we can prove by using the similar way.
Define the stopping time Obviously, when . And define the stopping time . If for , then ; that is, . Indeed, and , we define and . Note that In fact, by and being continuous and the denseness of the rational number in , we havefor all . That is for and one has . However, by the definition of and we have .
In the following we shall prove . Set . By continuity of and we have Obviously, . So, we have . Hence, for and we have . Using as a lower solution of SDE (10), we haveHence, Let us take . By the Tanaka formula (refer to [3]) we have where denotes local time at the point for . By the definition of local time, one can prove easily that , for on . So, by (using the definition ) we have Since for and we have , by (18) we haveUsing (16), we haveBy the stochastic Gronwall inequality (e.g., Lemma 2.1 [14]), we have By we haveSo, using (16) once again we have That is on a.s. Hence, . The proof is completed.

3. Existence of Strong Solutions

In this section, we will show the existence of the solution for SDEs with discontinuous drift coefficients. The method of the proof of our main result is based on Amann’s fixed point theorem (e.g., Theorem 11.D [16]), so we introduce it in the following.

Lemma 7. Suppose that
(1) the mapping is monotone increasing on an ordered set
(2) every chain in has a supremum
(3) there is an element for which
Then has a smallest fixed point in the set .

The following theorem is our main result.

Theorem 8. Let be predictable. Suppose that is an increasing function in and satisfies and there exists a predictable process , such that where Moreover, suppose that and are upper and lower solutions of the SDEsuch that
Then there is at least a strong solution which satisfies that for holds with probability 1.

Proof. Let be a space of adapted and continuous processes and define the order relation : for . We consider a subset of the space For arbitrary fixed , we consider the following equation: by Theorem 6 there exists a unique strong solution . Define a mapping and . To complete the proof it is enough to show has a fixed point.
Since is an increasing function and is an upper solution of SDE (25), we have thatholds with probability 1 for . Then is also an upper solution of SDE (28). Similarly, we have that holds with probability 1 for such that is also a lower solution of SDE (28). Hence, using Theorem 6 we haveSince is arbitrary, we have and and . If is an increasing mapping, by Lemma 7 has a fixed point on . In fact, take and and set ; that is, Since is an increasing function, we have that holds with probability 1 for . Hence is an upper solution of the following equation: And by (29) is an upper solution of (34). Using Theorem 6 again, we havethat is, . Hence is an increasing function. The proof is completed.

Example 9. We consider the following SDE: with initial value . Obviously, . By Theorem 8, there exists at least one solution such that holds with probability 1.

Example 10. We have the SDE with initial value , where is a bounded function and is defined asIt is easy to show and are the lower solution and upper solution of (37), respectively. And is an increasing function in but is not continuous in , so we have that SDE (37) has a strong solution by using Theorem 8.

Conflicts of Interest

The author declares that they have no conflicts of interest.

Acknowledgments

This paper was supported by the Fundamental Research Funds for the Central Universities and the School of Statistics and Mathematics of CUFE.