Efficiently Reusing Monte Carlo Simulation Output in Repeated Experiments for Financial and Actuarial Applications

在金融和精算应用的重复实验中有效地重用蒙特卡洛模拟输出

• 批准号: RGPIN-2018-03755
• 负责人: Feng, Mingbin
• 金额: $ 1.17万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765757
• 关键词: Efficiently; Reusing; Monte; Carlo; Simulation

Consider a setting where simulation experiments are performed repeatedly, using the same simulation model but different input values. For example, in finance and insurance, simulation models support pricing and risk management decisions that are made periodically. In these settings, the standard practice is that each experiment is performed in isolation without using the output of previous experiments. We advocate a new simulation design paradigm, called green simulation, where one reuses output from previous experiments to answer new questions or to enhance the quality of new answers. Green simulation entails a new perspective on the management of simulation experiments. In standard practice, running simulation is a computational expense. In green simulation, running simulation is a computational investment that provides future benefits.The proposed research program involves designing, analyzing, and testing new green simulation experiment designs. The impact of green simulation extends beyond the design and analysis of computer experiments to enterprise risk management, financial engineering, data science, machine learning, and artificial intelligence.The benefit of green simulation is greater computational efficiency. Our preliminary findings show that green simulation can yield estimators whose variance converges to zero in settings where the standard practice yields estimators whose variances do not converge. Experiments on complex actuarial and financial applications show that green simulation can achieve substantially higher accuracy than standard practice. Such improvement can greatly reduce data and model uncertainty in enterprise risk management for insurance companies.The innovation in green simulation lies in its temporal view of a sequence of repeated experiments, as opposed to the standard experiment design that views each experiment in isolation. The proposed green simulation algorithms will improve the efficiency of later experiments by storing and reusing the output of earlier experiments. The proposed research program has numerous promising ventures, some of them include: development of novel green simulation experiment designs; theoretical analysis of the proposed experiment designs; application of green simulation in financial and actuarial applications; integration of machine learning and artificial intelligence methods in green simulation experiments.The ultimate goal of this research is to enable simulation users to conduct simulation experiments faster and cheaper. Simulation modeling is widespread in many businesses and in many scientific and engineering research fields. It often requires intensive use of high-performance computing; this occupies a scarce resource and consumes electricity. Green simulation offers a new venue to conduct simulation experiments more efficiently and more environmentally friendly.

考虑这样一种设置，其中使用相同的模拟模型但不同的输入值重复执行模拟实验。例如，在金融和保险领域，模拟模型支持定期做出的定价和风险管理决策。在这些设置中，标准做法是每个实验都单独执行，而不使用以前实验的输出。我们倡导一种新的模拟设计范式，称为绿色模拟，其中一个人重复使用以前实验的输出来回答新的问题或提高新答案的质量。绿色仿真为仿真实验的管理带来了新的视角。在标准实践中，运行仿真是一项计算费用。在绿色仿真中，运行仿真是一项提供未来收益的计算投资，所提出的研究计划涉及设计、分析和测试新的绿色仿真实验设计。绿色模拟的影响从计算机实验的设计和分析扩展到企业风险管理、金融工程、数据科学、机器学习和人工智能。绿色模拟的好处是更高的计算效率。我们的初步发现表明，在标准实践产生的估计器的方差不收敛的情况下，绿色模拟可以产生方差收敛到零的估计量。对复杂精算和金融应用的实验表明，绿色模拟可以实现比标准实践高得多的精度。这种改进可以极大地降低保险公司企业风险管理中数据和模型的不确定性。绿色模拟的创新之处在于其对一系列重复试验的时间视图，而不是孤立地看待每个试验的标准试验设计。所提出的绿色模拟算法将通过存储和重用早期实验的输出来提高后续实验的效率。该研究项目有许多有前景的项目，其中包括：开发新的绿色模拟实验设计；对所提出的实验设计进行理论分析；绿色模拟在金融和精算应用中的应用；机器学习和人工智能方法在绿色模拟实验中的集成。本研究的最终目标是使模拟用户能够更快、更便宜地进行模拟实验。仿真建模在许多行业以及许多科学和工程研究领域都很普遍。它通常需要密集使用高性能计算；这占用了稀缺的资源并消耗了电力。绿色模拟提供了一个新的场所，可以更高效、更环保地进行模拟实验。