Description: Presented at October 1996 Annual Meeting. This discussion presents an introduction to Monte Carlo and why it is used for integration and valuation of complex formulas. Moreover, results using low-discrepancy sequences instead of the Monte Carlo simulation for pension fund analysis is shared.

Description: Summary of SOA research report "Optimizing Risk Retention" which applies parametric survival models and Monte Carlo simulation to a block of term life business.

Description: This paper introduces and illustrates a new version of the Monte Carlo method that has attractive properties for the numerical valuation of derivatives. Quasi-Monte Carlo methods use deterministic sequences that are quasi-random sequences that result in better convergence and deterministic error bounds.

Description: This is the abstract of the paper Quasi-Monte Carlo Methods in Numerical Finance. This paper introduces and illustrates a new version of the Monte Carl method that has attractive properties for the numerical valuation of derivatives. The traditional Monte Carlo method has proven to be a powerful and flexible tool for many types of derivatives calculations.

Description: At this session 114 PD of the Toronto Spring Meeting, participants learn how to create efficient simulation models in Excel for various problems arising in finance and insurance. From the Record of the Society of Actuaries, Volume 27, No. 2, Session

Description: This is a record of a session at the October 1975 Annual Meeting of the SOA. It discusses what can be done with a corporate model and how it can be used to solve problems. 1975 Record Vol. 1, No 4.

Description: The article and guidebook of the same name use a stochastic simulation model to examine potential impacts of public pension plan risk-sharing policies on government employers and plan members. Policies examined include simple policies such as COLAs and employee contributions that are contingent upon investment returns or funded ratios, and more-complex policies similar to those used in the Wisconsin Retirement System.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.