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.

考虑一种设置，其中使用相同的仿真模型但不同的输入值重复执行仿真实验。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。在这种情况下，标准做法是每个实验都单独进行，不使用以前实验的输出。我们提倡一种新的模拟设计范式，称为绿色模拟，在这种范式中，人们可以重用以前实验的输出来回答新问题或提高新答案的质量。绿色仿真为仿真实验的管理提供了新的视角。在标准实践中，运行仿真是一项计算开销。在绿色仿真中，运行仿真是一种提供未来收益的计算投资。提出的研究计划包括设计、分析和测试新的绿色模拟实验设计。绿色模拟的影响从计算机实验的设计和分析延伸到企业风险管理、金融工程、数据科学、机器学习和人工智能。绿色仿真的好处是提高了计算效率。我们的初步研究结果表明，在标准实践产生方差不收敛的估计量的设置中，绿色模拟可以产生方差收敛到零的估计量。在复杂的精算和金融应用中进行的实验表明，绿色仿真可以达到比标准实践高得多的精度。这样的改进可以大大降低保险公司在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它对一系列重复实验的时间视图，而不是孤立地看待每个实验的标准实验设计。提出的绿色仿真算法通过存储和重用早期实验的输出来提高后续实验的效率。提出的研究计划有许多有前途的项目，其中包括：开发新颖的绿色模拟实验设计；实验设计方案的理论分析；绿色模拟在金融和精算应用中的应用绿色仿真实验中机器学习与人工智能方法的融合。本研究的最终目标是使仿真用户能够更快、更便宜地进行仿真实验。仿真建模在许多企业和许多科学与工程研究领域都得到了广泛的应用。它通常需要大量使用高性能计算；这既占用稀缺资源，又消耗电力。绿色仿真为更高效、更环保地进行仿真实验提供了新的场所。