2,110 research outputs found

    Analysis of stochastic fluid queues driven by local time processes

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    We consider a stochastic fluid queue served by a constant rate server and driven by a process which is the local time of a certain Markov process. Such a stochastic system can be used as a model in a priority service system, especially when the time scales involved are fast. The input (local time) in our model is always singular with respect to the Lebesgue measure which in many applications is ``close'' to reality. We first discuss how to rigorously construct the (necessarily) unique stationary version of the system under some natural stability conditions. We then consider the distribution of performance steady-state characteristics, namely, the buffer content, the idle period and the busy period. These derivations are much based on the fact that the inverse of the local time of a Markov process is a L\'evy process (a subordinator) hence making the theory of L\'evy processes applicable. Another important ingredient in our approach is the Palm calculus coming from the point process point of view.Comment: 32 pages, 6 figure

    Some remarks on first passage of Levy processes, the American put and pasting principles

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    The purpose of this article is to provide, with the help of a fluctuation identity, a generic link between a number of known identities for the first passage time and overshoot above/below a fixed level of a Levy process and the solution of Gerber and Shiu [Astin Bull. 24 (1994) 195-220], Boyarchenko and Levendorskii [Working paper series EERS 98/02 (1998), Unpublished manuscript (1999), SIAM J. Control Optim. 40 (2002) 1663-1696], Chan [Original unpublished manuscript (2000)], Avram, Chan and Usabel [Stochastic Process. Appl. 100 (2002) 75-107], Mordecki [Finance Stoch. 6 (2002) 473-493], Asmussen, Avram and Pistorius [Stochastic Process. Appl. 109 (2004) 79-111] and Chesney and Jeanblanc [Appl. Math. Fin. 11 (2004) 207-225] to the American perpetual put optimal stopping problem. Furthermore, we make folklore precise and give necessary and sufficient conditions for smooth pasting to occur in the considered problem.Comment: Published at http://dx.doi.org/10.1214/105051605000000377 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

    The total mass of super-Brownian motion upon exiting balls and Sheu's compact support condition

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    We study the total mass of a d-dimensional super-Brownian motion as it first exits an increasing sequence of balls. The process of the total mass is a time-inhomogeneous continuous-state branching process, where the increasing radii of the balls are taken as the time parameter. We are able to characterise its time-dependent branching mechanism and show that it converges, as time goes to infinity, towards the branching mechanism of the total mass of a one-dimensional super-Brownian motion as it first crosses above an increasing sequence of levels. Our results allow us to identify the compact support criterion given in Sheu (1994) as a classical Grey condition (1974) for the aforementioned limiting branching mechanism.Comment: 28 pages, 2 figure

    Branching processes in random environment die slowly

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    Let Zn,n=0,1,...,Z_{n,}n=0,1,..., be a branching process evolving in the random environment generated by a sequence of iid generating functions f0(s),f1(s),...,% f_{0}(s),f_{1}(s),..., and let S0=0,Sk=X1+...+Xk,k1,S_{0}=0,S_{k}=X_{1}+...+X_{k},k\geq 1, be the associated random walk with Xi=logfi1(1),X_{i}=\log f_{i-1}^{\prime}(1), τ(m,n)\tau (m,n) be the left-most point of minimum of {Sk,k0}\left\{S_{k},k\geq 0\right\} on the interval [m,n],[m,n], and T=min{k:Zk=0}T=\min \left\{k:Z_{k}=0\right\} . Assuming that the associated random walk satisfies the Doney condition P(Sn>0)ρ(0,1),n,P(S_{n}>0) \to \rho \in (0,1),n\to \infty , we prove (under the quenched approach) conditional limit theorems, as nn\to \infty , for the distribution of Znt,Z_{nt}, Zτ(0,nt),Z_{\tau (0,nt)}, and Zτ(nt,n),Z_{\tau (nt,n)}, t(0,1),t\in (0,1), given T=nT=n. It is shown that the form of the limit distributions essentially depends on the location of τ(0,n)\tau (0,n) with respect to the point $nt.
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