{"title":"分数阶Ornstein–Uhlenbeck过程的参数估计","authors":"Fatima-Ezzahra Farah","doi":"10.1515/rose-2022-2079","DOIUrl":null,"url":null,"abstract":"Abstract We consider a problem of parameter estimation for the fractional Ornstein–Uhlenbeck model given by the stochastic differential equation d X t = - θ X t d t + d B t H {dX_{t}=-\\theta X_{t}dt+dB_{t}^{H}} , t ≥ 0 {t\\geq 0} , where θ > 0 {\\theta>0} is an unknown parameter to be estimated and B H {B^{H}} is a fractional Brownian motion with Hurst parameter H ∈ ( 0 , 1 ) {H\\in(0,1)} . We provide an estimator for θ, and then we study its strong consistency and asymptotic normality. The main tool in our proofs is the paper [I. Nourdin, D. Nualart and G. Peccati, The Breuer–Major theorem in total variation: Improved rates under minimal regularity, Stochastic Process. Appl. 131 2021, 1–20].","PeriodicalId":43421,"journal":{"name":"Random Operators and Stochastic Equations","volume":"30 1","pages":"161 - 170"},"PeriodicalIF":0.3000,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On parameter estimation of fractional Ornstein–Uhlenbeck process\",\"authors\":\"Fatima-Ezzahra Farah\",\"doi\":\"10.1515/rose-2022-2079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We consider a problem of parameter estimation for the fractional Ornstein–Uhlenbeck model given by the stochastic differential equation d X t = - θ X t d t + d B t H {dX_{t}=-\\\\theta X_{t}dt+dB_{t}^{H}} , t ≥ 0 {t\\\\geq 0} , where θ > 0 {\\\\theta>0} is an unknown parameter to be estimated and B H {B^{H}} is a fractional Brownian motion with Hurst parameter H ∈ ( 0 , 1 ) {H\\\\in(0,1)} . We provide an estimator for θ, and then we study its strong consistency and asymptotic normality. The main tool in our proofs is the paper [I. Nourdin, D. Nualart and G. Peccati, The Breuer–Major theorem in total variation: Improved rates under minimal regularity, Stochastic Process. Appl. 131 2021, 1–20].\",\"PeriodicalId\":43421,\"journal\":{\"name\":\"Random Operators and Stochastic Equations\",\"volume\":\"30 1\",\"pages\":\"161 - 170\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2022-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Random Operators and Stochastic Equations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/rose-2022-2079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Random Operators and Stochastic Equations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/rose-2022-2079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 1
摘要
考虑分数阶Ornstein-Uhlenbeck模型的参数估计问题,该模型由随机微分方程d _ X t=- θ _ X t _ d _t +d _ B_t H {dX_t{=-}\theta X_tdt{+}dB_t{^}H{, t≥0 }}t{\geq 0给出,}其中θ >0{\theta >0}是一个待估计的未知参数,B H{ B^{H}}是一个带有Hurst参数H∈(0,1){H\in(0,1)的分数阶布朗运动}。给出了θ的一个估计量,并研究了它的强相合性和渐近正态性。我们证明的主要工具是论文[1]。努尔丁,D. Nualart和G. Peccati,总变分的布鲁尔-梅奇定理:最小规则下的改进率,随机过程。中国科学:地球科学[j]。
On parameter estimation of fractional Ornstein–Uhlenbeck process
Abstract We consider a problem of parameter estimation for the fractional Ornstein–Uhlenbeck model given by the stochastic differential equation d X t = - θ X t d t + d B t H {dX_{t}=-\theta X_{t}dt+dB_{t}^{H}} , t ≥ 0 {t\geq 0} , where θ > 0 {\theta>0} is an unknown parameter to be estimated and B H {B^{H}} is a fractional Brownian motion with Hurst parameter H ∈ ( 0 , 1 ) {H\in(0,1)} . We provide an estimator for θ, and then we study its strong consistency and asymptotic normality. The main tool in our proofs is the paper [I. Nourdin, D. Nualart and G. Peccati, The Breuer–Major theorem in total variation: Improved rates under minimal regularity, Stochastic Process. Appl. 131 2021, 1–20].