{"title":"Ergodicity and Law-of-large numbers for the Volterra Cox-Ingersoll-Ross process","authors":"Mohamed Ben Alaya, Martin Friesen, Jonas Kremer","doi":"arxiv-2409.04496","DOIUrl":null,"url":null,"abstract":"We study the Volterra Volterra Cox-Ingersoll-Ross process on $\\mathbb{R}_+$\nand its stationary version. Based on a fine asymptotic analysis of the\ncorresponding Volterra Riccati equation combined with the affine transformation\nformula, we first show that the finite-dimensional distributions of this\nprocess are asymptotically independent. Afterwards, we prove a law-of-large\nnumbers in $L^p$(\\Omega)$ with $p \\geq 2$ and show that the stationary process\nis ergodic. As an application, we prove the consistency of the method of\nmoments and study the maximum-likelihood estimation for continuous and discrete\nhigh-frequency observations.","PeriodicalId":501084,"journal":{"name":"arXiv - QuantFin - Mathematical Finance","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Mathematical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We study the Volterra Volterra Cox-Ingersoll-Ross process on $\mathbb{R}_+$
and its stationary version. Based on a fine asymptotic analysis of the
corresponding Volterra Riccati equation combined with the affine transformation
formula, we first show that the finite-dimensional distributions of this
process are asymptotically independent. Afterwards, we prove a law-of-large
numbers in $L^p$(\Omega)$ with $p \geq 2$ and show that the stationary process
is ergodic. As an application, we prove the consistency of the method of
moments and study the maximum-likelihood estimation for continuous and discrete
high-frequency observations.