Poisson Hyperbolic Staircase in Discrete Time

TL;DR

This paper introduces a new discrete-time stochastic process as a counterpart to a known continuous-time model, analyzing its properties and deriving explicit formulas for distributions and martingales.

Contribution

It provides the first detailed analytical study of a discrete-time Poisson hyperbolic staircase process, including distribution formulas and martingale characterizations.

Findings

Derived closed-form expressions for marginal distribution and survival function.

Obtained explicit formulas for the probability generating function and Laplace transform.

Established conditions for constructing martingales related to the process.

Abstract

In this paper, we propose a novel stochastic process that serves as a natural discrete-time counterpart to the continuous-time model known as the ``Poisson hyperbolic staircase'' proposed by Levikson et al. (1999), and clarify its analytical properties. The proposed model is a Markov chain on the state space $(0,1]$. Its transition rule states that at each time step, it jumps downwards to a value less than or equal to the current state according to a continuous uniform distribution with a probability proportional to the current state, and otherwise remains in the same state. In the analysis of the continuous-time model, the scaling property based on the continuity of time and space serves as a powerful tool. However, for this discrete-time process, an essential analytical difficulty arises because this scaling property is inapplicable. To overcome this difficulty, we adopt an approach that directly evaluates recurrence relations and integral equations. First, starting from the conditional transition of this process, we derive closed-form expressions for the marginal distribution and the joint survival function. Next, focusing on the counting process representing the number of jump occurrences and the sum of the state variables, we provide exact closed-form expressions for the probability generating function and the Laplace transform. Furthermore, we clarify the necessary and sufficient conditions that a sequence of functions must satisfy to construct a martingale associated with this process, and present a concrete sequence of martingales.