Description: The panel discusses: 1. How fund transfers may affect expense recoveries through fee revenue 2.How transfers between fixed and variable funds may impact required capital 3. Product design features that affect the risk profile of the product and 4. Modeling considerations to quantify the policyholder behavior risk. From session 113PD of the 2001 Toronto Spring Meeting.

Description: The proposed revisions to the variable annuity statutory framework and the FASB targeted improvement for long duration contract, motivate life insurance companies to re-evaluate their hedging strategy. Recently proposed revisions of AG 43/VM-21 and C3 Phase II, reward companies that have a clearly defined hedging strategy, if the companies can demonstrate that the valuation model and capital model can estimate real hedge performance accurately. The speakers of this session will discuss the hedging implication of the proposed AG 43/VM-21 and C3 Phase II framework. Additionally, methodology computational requirements, back-testing results under different economic scenarios and the resulting hedge credits of different hedging strategies will be compared, including explicit dynamic hedge and implicit dynamic hedge. The speakers will also touch on the impact of rebalance frequency on the performance of dynamic hedge. Case studies will be presented to provide guidance on practical implementation.

Description: Presented at May 2002 Spring Meeting. Discusses the nature of the risks associated with equity-based and equity-linked products, and how those products can be managed with the use of capital market instruments.

Description: Presented at May 2002 Spring Meeting. This session provides an overview of the various methods used to manage the risks from guarantees of equity-based life and annuity products. Discussion includes the use of reinsurance, self-insurance and static/dynamic hedging.

Description: Regulatory change and increased focus on internal risk management are driving a renewed interest in model efficiency. In this session, we will review several new and existing tools, that show significant promise for advanced modeling of equity linked products. These include: • Clustering • Use of unique--and potentially a different number of--scenarios for each policy • Semi Monte Carlo (SMC) – this is a proprietary technique that is similar to a combination of Monte Carlo and numeric integration • Using a neural network approach to optimize some of the more complex and manual aspects of clustering and improve the fit of the clustered model • Combining SMC with the use of unique scenarios per policy