Description: An overview of guaranteed lifetime withdrawal benefits (GLWB) on registered index-linkedannuity (RILA) products and a stochastic analysis of market risks.

Description: Survey of appointed actuaries about asset adequacy testing techniques currently being used in 2012.

Description: The article describes the key components of actuarial model, leading practices in designing them, and characteristics of well-built models.

Description: Describes using the new multi-risk scenario generator to create sensitivity tests at moderately adverse probability levels, resulting in margins to use for PBR.

Description: Presentation at the 41st Actuarial Research Conference held on August 10-12, 2006 in Montreal, QC. This paper considers the problem of extending regime-switching models for asset returns to the situation with multiple asset markets. The conditional tail expectation CTE is highly sensitive to the assumed dependence structure between the regime models for the different markets. A Monte Carlo simulation is used to illustrate the cost of misspecification. The authors find that the model most commonly used by practitioners is worse than all of the alternatives that they investigate.

Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.

Description: Summary of the SOA research project on PBR Simplified Methods. Describes the multi-risk scenario generator, central estimate assumptions, assumption objectivity measures, and regulatory demonstrations per VM-20.

Description: This article deals with enterprise risk management and how the techniques contained within the discipline can help prevent unforeseen large mistakes in strategy, product design, and product management

Description: Producing a crystal-clear balance roll-forward on time, obtaining a fresh model output the first time or providing model results for your business partners with a tight turnaround all depend on one thing: how fast the model engine runs. Authors explored various reasons behind significant computational power consumption, and discussed different ways to speed the computational process such as in-force population compression, model use case consolidation, code simplification and streamlining, and infrastructure automation, etc.