On the Expected Discounted Penalty Function Using Physics-Informed Neural Network

IF 1.3 4区 数学 Q1 MATHEMATICS
Jiayu Wang, Houchun Wang
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引用次数: 0

Abstract

We study the expected discounted penalty at ruin under a stochastic discount rate for the compound Poisson risk model with a threshold dividend strategy. The discount rate is modeled by a Poisson process and a standard Brownian motion. By applying the differentiation method and total expectation formula, we obtain an integrodifferential equation for the expected discounted penalty function. From this integrodifferential equation, a renewal equation and an asymptotic formula satisfied by the expected discounted penalty function are derived. In order to solve the integrodifferential equation, we use a physics-informed neural network (PINN) for the first time in risk theory and obtain the numerical solutions of the expected discounted penalty function in some special cases of the penalty at ruin.
利用物理信息神经网络研究预期贴现惩罚函数
我们研究了具有门槛股息策略的复合泊松风险模型在随机贴现率条件下毁灭时的预期贴现惩罚。贴现率的模型是泊松过程和标准布朗运动。通过运用微分法和总期望公式,我们得到了期望贴现惩罚函数的积分微分方程。根据这个积分微分方程,可以得出预期贴现惩罚函数所满足的更新方程和渐近公式。为了求解这个整微分方程,我们在风险理论中首次使用了物理信息神经网络(PINN),并得到了预期贴现惩罚函数在一些毁灭惩罚特殊情况下的数值解。
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来源期刊
Journal of Mathematics
Journal of Mathematics Mathematics-General Mathematics
CiteScore
2.50
自引率
14.30%
发文量
0
期刊介绍: Journal of Mathematics is a broad scope journal that publishes original research articles as well as review articles on all aspects of both pure and applied mathematics. As well as original research, Journal of Mathematics also publishes focused review articles that assess the state of the art, and identify upcoming challenges and promising solutions for the community.
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