Fast and Efficient Nested Simulation for Large Variable Annuity Portfolios
Author | : X. Sheldon Lin |
Publisher | : |
Total Pages | : 37 |
Release | : 2019 |
ISBN-10 | : OCLC:1304236225 |
ISBN-13 | : |
Rating | : 4/5 ( Downloads) |
Download or read book Fast and Efficient Nested Simulation for Large Variable Annuity Portfolios written by X. Sheldon Lin and published by . This book was released on 2019 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nested-simulation is commonly used for calculating the predictive distribution of the total variable annuity (VA) liabilities of large VA portfolios. Due to the large numbers of policies, inner-loops and outer-loops, running the nested-simulation for a large VA portfolio (100K+) is extremely time consuming and often prohibitive. In this paper, the use of surrogate models is incorporated into the nested-simulation algorithm so that the relationship between the inputs and the outputs of a simulation model is approximated by various statistical models. As a result, the nested-simulation algorithm can be run with much smaller numbers of different inputs. Specifically, a spline regression model is used to reduce the number of outer-loops and a model-assisted finite population estimation framework is adapted to reduce the number of policies in use for the nested-simulation. From simulation studies, our proposed algorithm is able to accurately approximate the predictive distribution of the total VA liability at a significantly reduced running time.