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Backward doubly stochastic Volterra integral equations (BDSVIEs, for short) are introduced and studied systematically. Well-posedness of BDSVIEs in the sense of introduced M-solutions is established. A comparison theorem for BDSVIEs is…

Probability · Mathematics 2019-06-26 Yufeng Shi , Jiaqiang Wen , Jie Xiong

Mean-field backward stochastic Volterra integral equations (MF-BSVIEs, for short) are introduced and studied. Well-posedness of MF-BSVIEs in the sense of introduced adapted M-solutions is established. Two duality principles between linear…

Probability · Mathematics 2011-07-06 Yufeng Shi , Tianxiao Wang , Jiongmin Yong

For backward stochastic Volterra integral equations (BSVIEs, for short), under some mild conditions, the so-called adapted solutions or adapted M-solutions uniquely exist. However, satisfactory regularity of the solutions is difficult to…

Probability · Mathematics 2018-02-13 Tianxiao Wang , Jiongmin Yong

Backward stochastic Volterra integral equations (BSVIEs in short) are studied. We introduce the notion of adapted symmetrical solutions (S-solutions in short), which are different from the M-solutions introduced by Yong [17]. We also give…

Probability · Mathematics 2010-05-31 Tianxiao Wang , Yufeng Shi

In this paper, the notion of singular backward stochastic Volterra integral equations (singular BSVIEs for short) in infinite dimensional space is introduced, and the corresponding well-posedness is carefully established. A class of…

Optimization and Control · Mathematics 2023-12-08 Tianxiao Wang , Mengliang Zheng

Infinite horizon backward stochastic Volterra integral equations (BSVIEs for short) are investigated. We prove the existence and uniqueness of the adapted M-solution in a weighted $L^2$-space. Furthermore, we extend some important known…

Probability · Mathematics 2021-10-28 Yushi Hamaguchi

In [J. Wen, Y. Shi, Stat. Probab. Lett. 156 (2020) 108599] the authors first introduced a kind of anticipated backward stochastic Volterra integral equations (anticipated BSVIEs, for short). By virtue of the duality principle, it is found…

Probability · Mathematics 2026-05-13 Bixuan Yang , Tiexin Guo

In this paper, we provide variation of constants formulae for linear (forward) stochastic Volterra integral equations (SVIEs, for short) and linear Type-II backward stochastic Volterra integral equations (BSVIEs, for short) in the usual…

Probability · Mathematics 2022-10-24 Yushi Hamaguchi

This paper aims to study a new class of integral equations called backward doubly stochastic Volterra integral equations (BDSVIEs, for short). The notion of symmetrical martingale solutions (SM-solutions, for short) is introduced for…

Probability · Mathematics 2019-09-11 Jiaqiang Wen , Yufeng Shi

We introduce and study a new type of integral equations called anticipating backward stochastic Volterra integral equations (anticipating BSVIEs). In these equations the generator involves not only the present values but also the future…

Probability · Mathematics 2016-06-01 Jiaqiang Wen , Yufeng Shi

For a backward stochastic differential equation (BSDE, for short), when the generator is not progressively measurable, it might not admit adapted solutions, shown by an example. However, for backward stochastic Volterra integral equations…

Probability · Mathematics 2022-06-28 Hanxiao Wang , Jiongmin Yong , Chao Zhou

In this paper, we study the Backward stochastic Volterra integral equation driven by G-Brownian motion (G-BSVIE). By adopting a different backward iteration method, we construct the approximating sequences on each local interval. With the…

Probability · Mathematics 2025-12-30 Bingru Zhao , Mingshang Hu

Optimal control problems of forward-backward stochastic Volterra integral equations (FBSVIEs in short) are formulated and studied. A general duality principle is established for linear backward stochastic integral equation and linear…

Optimization and Control · Mathematics 2014-05-01 Yufeng Shi , Tianxiao Wang , Jiongmin Yong

Optimal control problems of forward-backward stochastic Volterra integral equations (FBSVIEs, in short) with closed control regions are formulated and studied. Instead of using spike variation method as one may imagine, here we turn to…

Optimization and Control · Mathematics 2016-02-19 Tianxiao Wang , Haisen Zhang

In this paper, we establish existence, uniqueness, and regularity properties of the solutions to multi-dimensional backward stochastic Volterra integral equations (BSVIEs), whose (possibly random) generator reflects nonlinear dependence on…

Probability · Mathematics 2025-01-09 Qian Lei , Chi Seng Pun

In this paper, we establish the relationship between backward stochastic Volterra integral equations (BSVIEs, for short) and a kind of non-local quasilinear (and possibly degenerate) parabolic equations. We first introduce the extended…

Probability · Mathematics 2019-08-21 Hanxiao Wang

Motivated by the optimality system associated with controlled (forward) Volterra integral equations (FVIEs, for short), the well-posedness of coupled forward-backward Voterra integral equations (FBVIEs, for short) is studied. The main…

Optimization and Control · Mathematics 2024-12-06 Wenyang Li , Hanxiao Wang , Jiongmin Yong

In this paper we study the unique solvability of backward stochastic Volterra integral equations (BSVIEs in short), in terms of both the M-solutions introduced in [17] and the adapted solutions in [6], [12] or [14]. A general existence and…

Probability · Mathematics 2010-01-21 Tianxiao Wang , Yufeng Shi

We study the numerical approximation of backward stochastic Volterra integral equations (BSVIEs) and their reflected extensions, which naturally arise in problems with time inconsistency, path dependent preferences, and recursive utilities…

Probability · Mathematics 2025-11-26 Nacira Agram , Giulia Pucci

In this paper, we study a class of Type-II backward stochastic Volterra integral equations (BSVIEs). For the adapted M-solutions, we obtain two approximation results, namely, a BSDE approximation and a numerical approximation. The BSDE…

Probability · Mathematics 2023-03-27 Yushi Hamaguchi , Dai Taguchi
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