Description: With seemingly unending demands on life & annuity companies for more intense and complex calculations, as well as greater sophistication around analytics, and all of this done at speed, never before has it been so important to seek out maximum efficiency in constructing models. In this session the panelists examine how the combination of various model efficiency approaches, pulling in many different facets of modeling and making it flexible enough to dial up or down various modeling components, can bring about incredible results. Not only does it become possible to produce and expand upon financial risk metrics via a building block approach, but also enables life insurers to produce analytics and dashboards to support strategic decisions that were simply not possible or cost effectively viable before.

Description: Modeling Approaches and Challenges focuses on approaches to real world simulation and the challenges and considerations in real world simulation. Topics include: A frequency domain approach to build realistic multi-horizon scenarios. (Ortec approach) Cascade models structure linking models for major asset classes and economic variables. (Moody’s approach) Overview of models, calibrations & approaches within the context of different use cases. Deterministic & stochastic approaches. Importance of model re-calibration flexibility. We will also discuss specific insights into considerations and challenges in real world simulation, which include topics like: Setting capital market assumptions and/or calibration targets. Assumptions about correlation and causality. Calibrating the model to capital markets assumptions. Validating the model against capital market assumptions. Role of history, market data and stylized facts. Importance of back testing. Calibrating the model to multiple investment horizons. Inclusion of alterative asset classes such as private equity, infrastructure, and direct real estate. Associated issues of limited data, illiquidity and greater uncertainty about calibration targets.