Numerical Solutions to Stochastic Model and Their Applications
A special issue of Mathematical and Computational Applications (ISSN 2297-8747).
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1902
Special Issue Editors
Interests: stochastic process; numerical solutions on stochastic models; model estimation and model selection
Special Issues, Collections and Topics in MDPI journals
Interests: asymptotic behaviours of Markov process; large deviation principle; moderate deviation principle in statistical physics and population genetics
Interests: risk and ruin theory; stochastic optimal control in insurance and finance; applied stochastic processes; machine learning techniques in actuarial science
Special Issue Information
Dear Colleagues,
For decades, stochastic modeling has been widely applied in fields such as economics, finance, insurance, population dynamics, epidemiology, and engineering. The stochastic differential equation (SDE), random algorithm, and stochastic optimal control play central roles in solving various modeling and decision-making problems. For example, SDEs provide the essential stochastic modeling device for modern financial theory and sampling algorithms in machine learning. In addition, most of the practical problems in finance and insurance are stochastic optimal control problems, in which optimal decisions (controls) are made based on information from the underlying dynamics over time. Such problems can be tackled with dynamic programming and Hamilton–Jacobi‒Bellman (HJB) equations or backward stochastic differential equations (BSDEs). However, for most of the applications, the underlying dynamics are so complex that the associated (B)SDEs or HJB equations cannot be analytically solved, thus necessitating the use of numerical methods for finding their approximated solutions. In machine learning and statistics, efficient algorithms usually rely heavily on fast sampling schemes, which account for most of the running times.
This Special Issue is devoted to collect contributions in numerical methods, random algorithms, and solutions of stochastic modeling and their applications. The topics include numerical solutions for various SDEs and BSDEs and their applications in finance and insurance, numerical solutions for stochastic optimal control problems, deep learning and reinforcement learning algorithms for BSDEs, optimal control, optimal stopping problems, etc.
Prof. Dr. Conghua Wen
Dr. Youzhou Zhou
Dr. Ran Xu
Prof. Dr. Zhuo Jin
Guest Editors
Manuscript Submission Information
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