A recursive representation for decoupling time-state dependent jumps from jump-diffusion processes

Qinjing Qiu, Reiichiro Kawai
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引用次数: 1

Abstract

AbstractWe establish a recursive representation that fully decouples jumps from a large class of multivariate inhomogeneous stochastic differential equations with jumps of general time-state dependent unbounded intensity, not of Lévy-driven type that essentially benefits a lot from independent and stationary increments. The recursive representation, along with a few related ones, are derived by making use of a jump time of the underlying dynamics as an information relay point in passing the past on to a previous iteration step to fill in the missing information on the unobserved trajectory ahead. We prove that the proposed recursive representations are convergent exponentially fast in the limit, and can be represented in a similar form to Picard iterates under the probability measure with its jump component suppressed. On the basis of each iterate, we construct upper and lower bounding functions that are also convergent towards the true solution as the iterations proceed. We provide numerical results to justify our theoretical findings.Keywords: Jump-diffusion processestime-state dependent jump ratePicard iterationpartial integro-differential equationsfirst exit times2020 Mathematics Subject Classifications: 91B3060G5165M1565N15 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by JSPS Grants-in-Aid for Scientific Research 20K22301 and 21K03347.
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跃变扩散过程中时间状态相关跃变解耦的递归表示
摘要我们建立了一个递归表示,它完全解耦了一大类多元非齐次随机微分方程的跳变,这些方程的跳变具有一般时间状态相关的无界强度,而不是由独立和平稳增量驱动的lsamv驱动型。递归表示以及一些相关的表示是通过利用底层动态的跳跃时间作为信息中继点,将过去传递到前一个迭代步骤,以填补前面未观察到的轨迹上的缺失信息而导出的。我们证明了所提出的递归表示在极限下是指数级快速收敛的,并且可以用类似于概率测度下抑制跳跃分量的Picard迭代的形式表示。在每次迭代的基础上,我们构造上界和下界函数,随着迭代的进行,它们也收敛于真解。我们提供数值结果来证明我们的理论发现。关键词:跳跃-扩散过程时间-状态依赖跳跃率ard迭代偏积分-微分方程首次退出时间2020数学学科分类:91B3060G5165M1565N15披露声明作者未报告潜在利益冲突。本研究得到了JSPS科学研究资助项目20K22301和21K03347的部分资助。
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
42
审稿时长
>12 weeks
期刊介绍: Stochastics: An International Journal of Probability and Stochastic Processes is a world-leading journal publishing research concerned with stochastic processes and their applications in the modelling, analysis and optimization of stochastic systems, i.e. processes characterized both by temporal or spatial evolution and by the presence of random effects. Articles are published dealing with all aspects of stochastic systems analysis, characterization problems, stochastic modelling and identification, optimization, filtering and control and with related questions in the theory of stochastic processes. The journal also solicits papers dealing with significant applications of stochastic process theory to problems in engineering systems, the physical and life sciences, economics and other areas. Proposals for special issues in cutting-edge areas are welcome and should be directed to the Editor-in-Chief who will review accordingly. In recent years there has been a growing interaction between current research in probability theory and problems in stochastic systems. The objective of Stochastics is to encourage this trend, promoting an awareness of the latest theoretical developments on the one hand and of mathematical problems arising in applications on the other.
期刊最新文献
Monotone iterative technique for evolution equations with delay and nonlocal conditions in ordered Banach space Well-posedness for anticipated backward stochastic Schrödinger equations Infinite horizon impulse control of stochastic functional differential equations driven by Lévy processes Complete f -moment convergence for sums of asymptotically almost negatively associated random variables with statistical applications A recursive representation for decoupling time-state dependent jumps from jump-diffusion processes
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