A moderate deviation principle for stochastic Volterra equation

Yumeng Li, Ran Wang, Nian Yao and Shuguang Zhang

Statistics & Probability Letters, 2017, vol. 122, issue C, 79-85

Abstract: In this paper, we establish a moderate deviation principle for stochastic Volterra equation by using the weak convergence approach. A maximal inequality for stochastic integral plays an important role. As an application, we give an interesting example: a stochastic differential equation driven by fractional Brownian motion.

Keywords: Stochastic Volterra equations; Moderate deviation principle; Weak convergence method (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715216302413
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:122:y:2017:i:c:p:79-85

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2016.10.033

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().