Sums of independent Poisson subordinators and their connection with strictly \(\alpha \)-stable processes of Ornstein-Uhlenbeck type
type: Item
instance of: scholarly article
title: Sums of independent Poisson subordinators and their connection with strictly \(\alpha \)-stable processes of Ornstein-Uhlenbeck type
zbMATH Open document ID: 1189.60163
author: O. V. Rusakov
published in: Journal of Mathematical Sciences (New York)
publication date: 2009-11-13T00:00:00Z
review text: Let \(\{\xi\}=\{\xi_0, \xi_1, \dots\}\) be a sequence of i.i.d., random values. Let \(\Pi =\Pi(s, \lambda ), s\geq 0\) be a Poisson random process with constant intensity \(\lambda >0\). All random processes and variables are independent in this paper. Consider a random change of time in sequence \(\{\xi\} =\{\xi_i, i=0, 1,\dots\}\) determinated by a subordinator \(\Pi\), i.e., consider the process \(\Psi_\Pi(s)=\xi_{\Pi(s)}, s\geq 0\) of Poisson random index. It is the process with piecewise constant and continuous from the right sample paths, defined on \(\mathbb R_+\). Process \(\Pi\) is called the leading (or guiding) process and the sequence \(\{\xi\}\) is forming for process \[ \Psi_\Pi(s)=\sum^{\infty}_{j=0}\xi_j{\mathbf 1} \{\Pi(s)=j\}. \] It is the Markov process. Its transition probability together with Fourier transformation are obtained. The process \(\Psi_\Pi(s)\) is not a process with independent increments. The relation of the characteristic function of the random vector \(\eta=(\xi_{\Pi(r)}\); \(\xi_{\Pi(r+s)})\in R^2, r>0, s>0\) and that of random vector \(\xi_0\) is presented. Examples are considered when the i.i.d. values in the forming sequence obey normal, stable distribution or take values \(\pm 1\) with probability \(\frac{1}{2}\). Sums of i.i.d. processes \(\xi_{{\Pi}_i}(s), 1\leq i\leq N\) are considered in section~2. Let \(\mathbb{E}\xi_0=0\) and \(D\xi_0=1\). Denote \[ U_N(s)=\frac{1}{\sqrt{N}}\sum^N_{i=j}\xi_{\Pi_i(s)}. \] As \(N\to\infty\), these sums behave like Ornstein-Uhlenbeck processes with viscosity coefficient \(\lambda\). For \(N, s>0, r>0\) \[ \text{Cov}.\big(U_N(r), U_N(r+s)\big)=\exp (-\lambda s). \] In case the forming sequence consists of i.i.d. random values with stictly \(\alpha\)-stable distributions with \(\Phi_\xi(\tau)=\exp\{-|\tau|^\alpha\}, 0<\alpha\leq 2\), \[ L_N(s)=\frac{1}{N^{1/\alpha}}\;\sum^N_{j=1}\,\xi_{\Pi_j(s)} \] are stationary processes. The characteristic function of limit distribution of random vector \…
reviewed by: Nijolė Kalinauskaitė
Mathematics Subject Classification ID: 60J99
Mathematics Subject Classification ID: 60G99
Mathematics Subject Classification ID: 60G52
zbMATH DE Number: 5632986
zbMATH Keywords: processes of random index
zbMATH Keywords: sums of random processes
zbMATH Keywords: Markov processes
zbMATH Keywords: Ornstein-Uhlenbeck type \(\alpha \)-stable processes
MaRDI profile type: Publication
full work available at URL: s10958-009-9447-6
OpenAlex ID: W1963783584
cites work: Q3091938
cites work: Superposition of Ornstein-Uhlenbeck type processes
cites work: Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics. (With discussion)
DOI: 10.1007/S10958-009-9447-6
description: scientific article; zbMATH DE number 5632986