Description: The NAIC has adopted revised VM-21 and AG43 for variable annuities, effective Jan. 1, 2020. The panel of presenters will provide background and an overview of the revisions, discuss detailed aspects of the new CTE Amount Standard Projection and prescribed assumptions, as well as discuss implementation considerations and the implications to hedging. In addition, the panelists will share progress on VM-23, principle-based reserves for non-variable annuities.

Description: The speaker of this session will provide a bottom up analysis and categorization of the $200 billion of volatility control funds related investments while recapping volatility control fund trading around volatility spikes (including Aug. 2015 and June 2016). The assessment of potential market impact of the volatility control funds’ rebalancing activity will also be discussed.

Description: This article provides the reader with some insights regarding tax planning under the new required methodology.

Description: This article briefly describes the guarantees available within the segregated Fund products sold in Canada along with the associated risks and cost factors.

Description: The presentation will outline the different possibilities insurance companies have to hedge their equity risk for the various products.

Description: Important tools used in property & casualty P&C insurance—predictive modeling and data mining—can be applied to more effectively model policyholder behavior risks in variable annuity VA contracts. In this article, a case study is used to demonstrate how a predictive modeling approach can improve upon traditional methods used to model VA lapse behavior.

Description: This article discusses a new procedure for correcting inadvertent failures to satisfy the diversification requirements applicable to variable annuity and life insurance contracts under section 817h of the Internal Revenue Code.

Description: This session will explore several critical elements in developing a sustainable and coherent framework to translate complex annuity (VA and FIA) policyholder behavior experience data to assumption models using company- and industry-level data, as well as inforce management techniques to handle related risks.

Description: With advancement of computing technology and innovative product design, creating dependent and nested stochastic scenarios to accurately predict insurance business has become more feasible and affordable than it used to be. However, can technology and research be accessible, applicable and beneficial to all the stakeholders? Sharing research findings and its validation tools has been our interest and focus, and even a resource-saving strategy. The presenters will share a case study on a segregated fund with hedging strategy. We investigate the reserve calculation for its GMMB in an extensive but simplified stochastic model so that our method is adoptable, applicable and extendable to real-word segregated fund/separate account model. The segregated fund model theory and implementation will be presented and its cash flows will be simulated and analyzed for reserve calculation. Varying parameter assumptions will be tested and to be modified for real-world segregated fund with hedging risk for guaranteed benefits. At the conclusion of the session, attendees will gain basic understanding of and access to: - The option pricing model and its application to the segregated fund (variable annuity's separate account) guarantee - Stochastic scenario generators for the interest rates and equity returns - R-code to program from the scenarios simulations to cash flows of hedging errors - Reserve calculations and its sensitivity to various parameter values and - Comparison of methods to increasing efficiency level and the accuracy

Description: 1999 SOA Annual Meeting, San Francisco. The panelists provided a brief description of the guaranteed benefits offered on Variable Annuities in both the U.S. and Canada, followed by an in-depth comparison of current issues, including risk assessment, reserving, and cash-flow testing.