Description: Managed strategies as underlying investments in VA/FIA products have seen spectacular growth since the financial crisis. The speakers of these sessions will share their views on the development of this market and what is expected to unfold over the next 12 months in regard to the next generation of products and fund-based strategies.

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

Description: There is no “industry standard” approach to managing the vega (volatility) risk inherent in Variable Annuity Guarantees, Fixed Indexed Annuities and other equity-linked obligations. In this session, we will introduce and analyze a range of reinsurance solutions, including full vega risk retention, full vega risk transfer and more sophisticated or custom structures that split the vega risk between the insurer and the reinsurer in various ways. We will also explore other approaches in bespoke risk coverage and show the impact of different assumptions on a generic “VA Guarantee Cost Estimate” that has been developed by Munich Re.

Description: Reinsurance has been available for VA Guarantees for over 20 years. During that time, the reinsurance market for VA Guarantees has undergone frequent and sometimes violent transformations. Where does the market stand today? What forms and structures of reinsurance are available? Which VA Guarantee designs and features are easier to reinsure? Is reinsurance offered on both newly issued policies as well as in force/legacy books, and which vintages are difficult to reinsure? In an age of sophisticated hedging programs, what role does a reinsurer play in helping a VA writer manage this highly volatile risk? In this session, we will hear from a reinsurer that is actively writing new VA Guarantee reinsurance treaties in the U.S. and around the world, and from a VA writer that has successfully partnered with a reinsurer to enhance its VA product offering.

Description: The speaker will discuss the training of neural networks to compute future Greeks of liabilities (or options) without the use of nested stochastics.

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: 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