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Some Refinements of Existence Results for SPDEs Driven by Wiener Processes and Poisson Random Measures

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  • Stefan Tappe

Abstract

We provide existence and uniqueness of global (and local) mild solutions for a general class of semilinear stochastic partial differential equations driven by Wiener processes and Poisson random measures under local Lipschitz and linear growth (or local boundedness, resp.) conditions. The so-called “method of the moving frame” allows us to reduce the SPDE problems to SDE problems.

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  • Stefan Tappe, 2012. "Some Refinements of Existence Results for SPDEs Driven by Wiener Processes and Poisson Random Measures," International Journal of Stochastic Analysis, Hindawi, vol. 2012, pages 1-24, December.
    • Handle: RePEc:hin:jnijsa:236327
    DOI: 10.1155/2012/236327
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    Cited by:

    1. Pham, Viet Son & Chong, Carsten, 2018. "Volterra-type Ornstein–Uhlenbeck processes in space and time," Stochastic Processes and their Applications, Elsevier, vol. 128(9), pages 3082-3117.
    2. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2020. "Accuracy of Deep Learning in Calibrating HJM Forward Curves," Papers 2006.01911, arXiv.org, revised May 2021.
    3. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021. "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, vol. 3(3), pages 209-248, December.

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