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

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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.