The short-term riskless interest rates started to develop from the late 1970s and gradually became one of the most fundamental and important quantities in financial markets. As the economy has evolved, many short-term interest rate models have also been proposed. Letting represent the short-term interest rates, we have the following seven well-known models:
1) Merton 
2) Vasicek 
3) Geometric Brownian motion
4) Cox, Ingersoll and Ross 
5) Brennan and Schwartz 
6) Cox, Ingersoll and Ross 
7) Constant Elasticity of Variance 
where λ, μ and σ are constants.
The above seven models can be combined with the following stochastic differential equation:
by changing the parameters λ, μ and γ.
Where , we all know that the Equation (1.1) is the well-known Vasicek model  . If its coefficient , there is a mean-reverting property. In other
words, the short-term interest rates will approach the mean in the
long term. When , the Equation (1.1) is the well-known mean-reverting square root process  . Like the Vasicek model, the mean-reverting square root process also has a mean-reverting property. More importantly, it is a non-negative random process. So it is widely used to model volatility, interest rates and other financial quantities. When , Mao et al.  discuss its analytical properties and strong convergence of numerical solutions. When , it is a highly sensitive mean-reverting process, Wu et al.  show the way to prove that the solution of Equation (1.1) is existence and uniqueness.
In the next section, we first prove the existence and uniqueness of the solution of Equation (1.1) when . In section three, we consider the existence and non-negative of the solution of Equation (1.1) when . Finally, we will give the conclusion.
2. The Unique Global Positive Solutions when γ ≥ 1
Throughout this paper, let be a complete probability space with a filtration satisfying the usual conditions. Let be a scalar Brownian motion defined on the probability space. We consider the mean-reverting γ-process:
with the initial value , where λ, μ and σ are positive and .
Theorem 2.1. For any given initial value , λ, μ and , there exists a unique positive global solution to Equation (2.1) on .
Proof: Clearly, the coefficients of (2.1) satisfy the local Lipchitz condition. Therefore, for any given initial value , there must exist a unique local solution , where is the explosion time. To show the solution is global, we next show that a.s.
Firstly, we choose a sufficient large integer , namely , and then we define the following stopping time,
Let , it is clear that is increasing as . Set , we get a.s. To complete the proof what we need to show is that a.s. To prove this result, for any constant T, if there is ,
namely , by the control of convergence theorem, then we can get , so there is , which is the result we need.
For , we define a C2-function by
It is easy to show that and as or . Apply the Itô formula yields
For , by the boundedness of polynomial, it is easy to show that there is a constant such that
Therefore, for ,
On the both sides of the formula, we take integrate from 0 to . And then taking expectations, we have
In the following, we use the proof by contradiction to show. If the previous conclusion is wrong, then there exists and , such that
So there is an integer ,
Due to , then there exists an integer , we get as . In addition, since is monotonically increasing, we can obtain . Set , from the above formula, we get as . For every , there is or
. It is easy to show that the function monotonically decreases in the interval , monotonically increases in the interval . So
Due to , we have as . This implies . We finish the proof.
3. The Unique Positive Solutions when γ Î [1/2, 1]
When , in order to prove the conclusion, we complete in two steps. The first is to prove the non-negative solution if the solution exists. The second is to prove the existence and uniqueness of solutions.
3.1. Non-Negative Solution
As mentioned earlier, like the well-known mean-reverting square root process  , it is widely used to model volatility, interest rates and other financial quantities. But there is a premise that is non-negative. So we will prove it is a non-negative solution.
We consider the mean-reverting γ-process:
with the initial value , where λ, μ and σ are positive and .
Lemma 3.1. For given any initial data , the solution of Equation (3.1) will never become negative with probability 1.
Proof: We consider the following equation
It is clear that the solution of Equation (3.2) is equivalent to the statement of the lemma 3.1.
In the following, we will show that the solution of Equation (3.2) will never become negative with probability 1 for any initial data .
Let , and for each integer ,
So it is easy to get the following result:
For every , there clearly exists a continuous function with support in such that
Then and has the following properties:
1) for , or otherwise ;
2) for , or otherwise ;
3) for .
It is easy to show the properties (1), (2) and (3).
Now for , apply the Itô formula yields
Then taking expectations in both sides, we have
Therefore, apply the properties (3), we have
Letting , we get that . So we have for . This implies that the Lemma 3.1 is true.
3.2. The Existence and Uniqueness of Solutions
The following result shows that the solution of Equation (3.1) is exist and unique.
Theorem 3.1. For any given initial value , λ, μ and , there exists a unique solution to Equation (3.1) on .
Proof: Clearly, the coefficients of (3.1) satisfy the linear growth condition. From I. Karatzas and S.E. Shreve  , we know that there is a weak solution for Equation (3.1). Next, we will show that the solution satisfies the track uniqueness.
It is easy to show that the following inequality is right:
with the .
Thus, we have
Similarly, the following inequality can be proved
So we have
It is easy to get the following result:
By the diffusion coefficient of Equation (3.1), we get
Referring to the theorem 19.5 of The Elements of Stochastic Analysis  , the theorem 3.1 is true. Therefore, the solution of Equation (3.1) is exist and unique. We finish the proof.
In this article, we proved the existence and uniqueness of the solution of Equation (1.1) when . And we get the existence and non-negative of the solution of Equation (1.1) when . Further, we can study the solution of the Equation (1.1) when .
 Mao, X., Truman, A. and Yuan, C. (2006) Euler-Maruyama Approximations in Mean-Reverting Stochastic Volatility Model under Regime-Switching. Journal of Applied Mathematics and Stochastic Analysis, 2, 1-20.
 Wu, F., Mao, X. and Chen, K. (2008) A Highly Sensitive Mean-Reverting Process in Finance and the Euler-Maruyama Approximations. Journal of Mathematical Analysis and Applications, 348, 540-554.