Description: A panel of actuaries and investment experts share their experience with the AG53 requirements for Asset Adequacy Testing. They will discuss challenging areas in the inaugural year of the consideration of complex assets as well as discuss potential areas of future regulatory expectations.

Description: Global life insurance companies started to utilize both external and internal reinsurance for a holistic group-wide capital and risk management. This is more evident in increasing use of reinsurance affiliates in Bermuda and/or reinsuring business to Bermuda-based external reinsurers.Come explore how varying and often competing objectives of capital and risk management is balanced or optimized through the use of internal and external reinsurance from a view of global direct life insurance and annuity writers who will present cases and share their objectives, concerns, and experience in a relatively comparable structure. Presenters will have a brief panel discussion on a couple of hot topics such as on-going enhancement of Bermuda's capital and reserve regulatory framework and use of internal reinsurance.Get ready to learn practical and emerging views of insurers on the effective use of internal and external reinsurance for risk and capital management.

Description: In this presentation, we will explore the latest investment trends shaping the life insurance industry today. We will start with an overview of the various types of assets and strategic asset allocations that are prevalent within the industry. Moving beyond the basics, we will delve into an advanced asset optimization tool that is revolutionizing how asset portfolios are constructed. This tool considers factors such as asset liability cash flow matching, target yields, durations, Risk-Based Capital (RBC) requirements, and asset type restrictions. As the life insurance sector places greater emphasis on sophisticated asset management and navigates increasingly complex portfolios, understanding and leveraging this optimization tool has become crucial. Join us to gain valuable insights into optimizing asset portfolios to meet the evolving demands of the life insurance industry.

Description: In this session, we will delve into the complexities of liquidity risk within insurance portfolios, focusing on how insurers manage this risk in an increasingly competitive and volatile market. With insights from industry leaders and real-world examples, attendees will gain a comprehensive understanding of the factors driving liquidity risk and the strategies used to mitigate it. We will explore the impact of economic policies, market dynamics, and regulatory changes on liquidity risk management.

Description: Inflation can impact retirement and retirees in a variety of ways. With inflation as a reality in the current economic environment, the Society of Actuaries Research Institute's Aging and Retirement Strategic Research Program has explored this issue in recently published research and essays. This webcast will present results of that work, sharing different aspects of inflation's impact on retirement including savings, investment choices and the impact on retirees themselves.

Description: Life and annuity insurers have been steadily adding more structured securities and other “complex assets” to their investment portfolios over the past decade. This Webcast will review what these assets are, why insurers use them, and what risks they introduce.

Description: The surge in interest rates over the past few years presents opportunities for life insurers to enhance their Asset and Liability Management (ALM) positions through asset rebalancing to narrow the duration gap and secure higher risk-adjusted yields. This webcast introduces a robust, efficient and readily-executable, liability-driven strategic asset allocation (SAA) framework and implementation considerations. At the conclusion of the webcast, attendees will learn about the key objectives and practical considerations for a successful SAA.

Description: This paper introduces actuaries and others to dynamic stochastic general equilibrium (DSGE) models, a type of macroeconomic model. Authored by Kailan Shang, the paper illustrates how DSGE models can be used in economic scenario generation to reflect the cause and effect relationships that occur in the economic system.

Description: This paper focuses on business risks rather than market, credit and operational risks. The author proposes a bottom-up approach for modelling and measuring business risk where the dynamic of the underlying earnings is described in a continuous-time model.

Description: A central problem for regulators and risk managers concerns the risk assessment of an aggregate portfolio defined as the sum of d individual dependent risks Xi. This problem is mainly a numerical issue once the joint distribution of (X1,X2, . . . ,Xd) is fully specified. Unfortunately, while the marginal distributions of the risks Xi are often known, their interaction (dependence) is usually either unknown or only partially known, implying that any computed risk measure of the portfolio is subject to model uncertainty. Previous academic research has focused on the maximum and minimum possible values of a given risk measure of the portfolio, in the case in which only the marginal distributions are known. This approach leads to wide bounds, as all information on the dependence is ignored. In this paper, we integrate in a natural way available information on the multivariate dependence and provide easy-to-compute bounds for the risk measure at hand. We observe that incorporating the information of a well-fitted multivariate model may, or may not, lead to much tighter bounds, a feature that also depends on the risk measure used. We illustrate this point by showing that the Value-at-Risk at a very high confidence level (as used in Basel II) is typically prone to very high model risk, even if one knows the multivariate distribution almost completely. Our results make it possible to determine which risk measures can benefit from adding dependence information (i.e., leading to narrower bounds when used to assess portfolio risk), and, hence, to identify those models for which it would be meaningful to develop accurate multivariate models.