A0563
Title: Risk measurement of guaranteed minimum benefits via a moment-based density approximation approach
Authors: Rogemar Mamon - University of Western Ontario (Canada) [presenting]
Abstract: A modeling framework is presented for the dependent risk factors of the guaranteed minimum maturity benefit (GMMB) and guaranteed minimum income benefit (GMIB) riders, and their corresponding loss functions are then characterized. A range of risk measures and their mathematical properties are revisited. Two probability-density-approximation methods are developed akin to: (i) Baseline density polynomial (BDP) and (ii) generalized Pearson family (GPF). These methods are customized for estimating the distribution of the GMMB and GMIB loss random variables. A numerical experiment is conducted to illustrate the advantages of the proposed density-based approach. In particular, the risk measures are calculated using the BDP and GPF methods, and the results are benchmarked with those generated by the standard Monte Carlo simulation. Findings confirm the superior accuracy of the proposed approach in the risk measurement of GMMB and GMIB.
