Path Dependent Feynman-Kac Formula for Forward Backward Stochastic Volterra Integral Equations

作者
Wang, Hanxiao,Yong, Jiongmin,Zhang, Jianfeng
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.2004.05825
摘要

This paper is concerned with the relationship between forward-backward stochastic Volterra integral equations (FBSVIEs, for short) and a system of (non-local in time) path dependent partial differential equations (PPDEs, for short). Due to the nature of Volterra type equations, the usual flow property (or semigroup property) does not hold. Inspired by Viens-Zhang \cite{Viens-Zhang-2019} and Wang-Yong \cite{Wang-Yong-2019}, auxiliary processes are introduced so that the flow property of adapted solutions to the FBSVIEs is recovered in a suitable sense, and thus the functional It\^o's formula is applicable. Having achieved this stage, a natural PPDE is found so that the adapted solution of the backward SVIEs admits a representation in terms of the solution to the forward SVIE via the solution to a PPDE. On the other hand, the solution of the PPDE admits a representation in terms of adapted solution to the (path dependent) FBSVIE, which is referred to as a Feynman-Kac formula. This leads to the existence and uniqueness of a classical solution to the PPDE, under smoothness conditions on the coefficients of the FBSVIEs. Further, when the smoothness conditions are relaxed with the backward component of FBSVIE being one-dimensional, a new (and suitable) notion of viscosity solution is introduced for the PPDE, for which a comparison principle of the viscosity solutions is established, leading to the uniqueness of the viscosity solution. Finally, some results have been extended to coupled FBSVIEs and type-II BSVIEs, and a representation formula for the path derivatives of PPDE solution is obtained by a closer investigation of linear FBSVIEs.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Hubert完成签到,获得积分10
1秒前
yby发布了新的文献求助10
2秒前
杜若发布了新的文献求助10
2秒前
4秒前
牛牛牛完成签到 ,获得积分10
5秒前
6秒前
6秒前
咿呀完成签到,获得积分10
8秒前
10秒前
10秒前
10秒前
11秒前
咿呀发布了新的文献求助10
12秒前
Spring完成签到,获得积分10
12秒前
YY230512发布了新的文献求助10
13秒前
小马甲应助arsenal采纳,获得10
13秒前
14秒前
15秒前
朴素懿轩发布了新的文献求助10
16秒前
解语花031发布了新的文献求助10
17秒前
爱lx发布了新的文献求助10
19秒前
salokim发布了新的文献求助10
21秒前
21秒前
21秒前
白忘幽发布了新的文献求助10
22秒前
22秒前
23秒前
orixero应助liguangfei采纳,获得10
23秒前
23秒前
25秒前
25秒前
26秒前
风清扬发布了新的文献求助10
26秒前
poletar发布了新的文献求助10
27秒前
27秒前
爱lx完成签到,获得积分10
29秒前
30秒前
Nari发布了新的文献求助10
30秒前
大力从云发布了新的文献求助10
30秒前
arizaki7发布了新的文献求助10
30秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Inorganic Chemistry Eighth Edition 1200
Free parameter models in liquid scintillation counting 1000
Anionic polymerization of acenaphthylene: identification of impurity species formed as by-products 1000
Standards for Molecular Testing for Red Cell, Platelet, and Neutrophil Antigens, 7th edition 1000
HANDBOOK OF CHEMISTRY AND PHYSICS 106th edition 1000
ASPEN Adult Nutrition Support Core Curriculum, Fourth Edition 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6312614
求助须知:如何正确求助?哪些是违规求助? 8129175
关于积分的说明 17034933
捐赠科研通 5369569
什么是DOI,文献DOI怎么找? 2850899
邀请新用户注册赠送积分活动 1828703
关于科研通互助平台的介绍 1680943