As for the backward and forward equation of nonhomogeneous(H, Q) -processes,we proof them in a new way. On the base of that, this paper gives the direct computational formalfor one dimensional distribution of the nonh...As for the backward and forward equation of nonhomogeneous(H, Q) -processes,we proof them in a new way. On the base of that, this paper gives the direct computational formalfor one dimensional distribution of the nonhomogeneous(H, Q) -process.展开更多
Empirical studies show that more and more short-term rate models in capturing the dynamics cannot be described by those classic ones. So the mean-reverting γ-process was correspondingly proposed. In most cases, its c...Empirical studies show that more and more short-term rate models in capturing the dynamics cannot be described by those classic ones. So the mean-reverting γ-process was correspondingly proposed. In most cases, its coefficients do not satisfy the linear growth condition;even they satisfy the local Lipschitz condition. So we still cannot examine its existence of solutions by traditional techniques. This paper overcomes these difficulties. Firstly, through using the function Lyapunov, it has proven the existence and uniqueness of solutions for mean-reverting γ-process when the parameter . Secondly, when , it proves the solution is non-negative. Finally, it proves that there is a weak solution to the mean-reverting γ-process and the solution satisfies the track uniqueness by defining a function ρ. Therefore, the mean-reverting γ-process has the unique solution.展开更多
As we know,many scholars have been involved in the study of stochastic processes,whichhave a wide range of applications,such as the Markov processes.In the study of Markov pro-cesses,minimal Markov chain(i.e.the minim...As we know,many scholars have been involved in the study of stochastic processes,whichhave a wide range of applications,such as the Markov processes.In the study of Markov pro-cesses,minimal Markov chain(i.e.the minimal homogeneous denumerable Markov process)got most mature.One of the characteristics of this kind of processes is that the stay time展开更多
A new class of stochastic processes--Markov skeleton processes is introduced, which have the Markov property on a series of random times. Markov skeleton processes include minimal Q processes, Doob processes, Q proces...A new class of stochastic processes--Markov skeleton processes is introduced, which have the Markov property on a series of random times. Markov skeleton processes include minimal Q processes, Doob processes, Q processes of order one, semi-Markov processes , piecewise determinate Markov processes , and the input processes, the queuing lengths and the waiting times of the system GI/G/1, as particular cases. First, the forward and backward equations are given, which are the criteria for the regularity and the formulas to compute the multidimensional distributions of the Markov skeleton processes. Then, three important cases of the Markov skeleton processes are studied: the (H, G, Π)-processes, piecewise determinate Markov skeleton processes and Markov skeleton processes of Markov type. Finally, a vast vistas for the application of the Markov skeleton processes is presented.展开更多
The problem of uniform dimensions for multi-parameter processes, which may not possess the uniform stochastic H?lder condition, is investigated. The problem of uniform dimension for multi-parameter stable processes is...The problem of uniform dimensions for multi-parameter processes, which may not possess the uniform stochastic H?lder condition, is investigated. The problem of uniform dimension for multi-parameter stable processes is solved. That is, ifZ is a stable (N,d, α)-process and αN ?d, then $$\forall E \subseteq \mathbb{R}_ + ^N , \dim Z\left( E \right) = \alpha \cdot \dim E$$ holds with probability 1, whereZ(E) = {x : ?t ∈E,Z t =x} is the image set ofZ onE. The uniform upper bounds for multi-parameter processes with independent increments under general conditions are also given. Most conclusions about uniform dimension can be considered as special cases of our results.展开更多
The classical Ray-Knight theorems for the Brownian motion determine the law of its local time process either at the first hitting time of a given value a by the local time at the origin,or at the first hitting time of...The classical Ray-Knight theorems for the Brownian motion determine the law of its local time process either at the first hitting time of a given value a by the local time at the origin,or at the first hitting time of a given position b by the Brownian motion.We extend these results by describing the local time process jointly for all a and b,by means of the stochastic integral with respect to an appropriate white noise.Our result applies toμ-processes,and has an immediate application:aμ-process is the height process of a Feller continuous-state branching process(CSBP)with immigration(Lambert(2002)),whereas a Feller CSBP with immigration satisfies a stochastic differential equation(SDE)driven by a white noise(Dawson and Li(2012));our result gives an explicit relation between these two descriptions and shows that the SDE in question is a reformulation of Tanaka’s formula.展开更多
文摘As for the backward and forward equation of nonhomogeneous(H, Q) -processes,we proof them in a new way. On the base of that, this paper gives the direct computational formalfor one dimensional distribution of the nonhomogeneous(H, Q) -process.
文摘Empirical studies show that more and more short-term rate models in capturing the dynamics cannot be described by those classic ones. So the mean-reverting γ-process was correspondingly proposed. In most cases, its coefficients do not satisfy the linear growth condition;even they satisfy the local Lipschitz condition. So we still cannot examine its existence of solutions by traditional techniques. This paper overcomes these difficulties. Firstly, through using the function Lyapunov, it has proven the existence and uniqueness of solutions for mean-reverting γ-process when the parameter . Secondly, when , it proves the solution is non-negative. Finally, it proves that there is a weak solution to the mean-reverting γ-process and the solution satisfies the track uniqueness by defining a function ρ. Therefore, the mean-reverting γ-process has the unique solution.
文摘As we know,many scholars have been involved in the study of stochastic processes,whichhave a wide range of applications,such as the Markov processes.In the study of Markov pro-cesses,minimal Markov chain(i.e.the minimal homogeneous denumerable Markov process)got most mature.One of the characteristics of this kind of processes is that the stay time
文摘A new class of stochastic processes--Markov skeleton processes is introduced, which have the Markov property on a series of random times. Markov skeleton processes include minimal Q processes, Doob processes, Q processes of order one, semi-Markov processes , piecewise determinate Markov processes , and the input processes, the queuing lengths and the waiting times of the system GI/G/1, as particular cases. First, the forward and backward equations are given, which are the criteria for the regularity and the formulas to compute the multidimensional distributions of the Markov skeleton processes. Then, three important cases of the Markov skeleton processes are studied: the (H, G, Π)-processes, piecewise determinate Markov skeleton processes and Markov skeleton processes of Markov type. Finally, a vast vistas for the application of the Markov skeleton processes is presented.
基金Project supported by Fujian Natural Science Foundation.
文摘The problem of uniform dimensions for multi-parameter processes, which may not possess the uniform stochastic H?lder condition, is investigated. The problem of uniform dimension for multi-parameter stable processes is solved. That is, ifZ is a stable (N,d, α)-process and αN ?d, then $$\forall E \subseteq \mathbb{R}_ + ^N , \dim Z\left( E \right) = \alpha \cdot \dim E$$ holds with probability 1, whereZ(E) = {x : ?t ∈E,Z t =x} is the image set ofZ onE. The uniform upper bounds for multi-parameter processes with independent increments under general conditions are also given. Most conclusions about uniform dimension can be considered as special cases of our results.
文摘The classical Ray-Knight theorems for the Brownian motion determine the law of its local time process either at the first hitting time of a given value a by the local time at the origin,or at the first hitting time of a given position b by the Brownian motion.We extend these results by describing the local time process jointly for all a and b,by means of the stochastic integral with respect to an appropriate white noise.Our result applies toμ-processes,and has an immediate application:aμ-process is the height process of a Feller continuous-state branching process(CSBP)with immigration(Lambert(2002)),whereas a Feller CSBP with immigration satisfies a stochastic differential equation(SDE)driven by a white noise(Dawson and Li(2012));our result gives an explicit relation between these two descriptions and shows that the SDE in question is a reformulation of Tanaka’s formula.
