2014
DOI: 10.1007/978-3-319-11292-3_1
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Wong-Zakai Approximation of Solutions to Reflecting Stochastic Differential Equations on Domains in Euclidean Spaces II

Abstract: The strong convergence of Wong-Zakai approximations of the solution to the reflecting stochastic differential equations was studied in [2]. We continue the study and prove the strong convergence under weaker assumptions on the domain.

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Cited by 3 publications
(8 citation statements)
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“…When the driving rough path is a geometric rough path, we can give another proof of the existence of solutions as in [3]. Below we use the notation C θ− = ∩ 0<ε<θ C θ−ε , C 1-var+,θ− = ∩ q>1,0<ε<θ C q-var,θ−ε .…”
Section: A Continuity Property Of the Solution Map And Support Theoremsmentioning
confidence: 99%
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“…When the driving rough path is a geometric rough path, we can give another proof of the existence of solutions as in [3]. Below we use the notation C θ− = ∩ 0<ε<θ C θ−ε , C 1-var+,θ− = ∩ q>1,0<ε<θ C q-var,θ−ε .…”
Section: A Continuity Property Of the Solution Map And Support Theoremsmentioning
confidence: 99%
“…It is proved in [3] that max 0≤t≤T |Y N t − Y t | converges to 0 in probability under (A) and (B). This and the moment estimate in [4] implies the almost sure convergence in Theorem 6.8 (2).…”
Section: Proof For Simplicity We Write Z(hmentioning
confidence: 99%
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“…For other references of reflected SDEs related with this paper, we refer the readers to [2,25,6,7,8,16,17,18,20,21]. In [1], we study Wong-Zakai approximations ( [23]) in the two cases, (i) the domain is convex, (ii) the conditions (A) and (B) are satisfied which are not contained in the result in [2].…”
Section: Preliminarymentioning
confidence: 99%