Efficient management of risk lies at the heart of the insurance industry. On a contractual level, this may be handled using various analytical methods. However, the complexity rapidly increases when considering the interplay between all of a company’s assets and liabilities, which becomes necessary for judging the financial stability of the operations as a whole. For solvency purposes, e.g. as stipulated under pillar one of the Solvency II-accord, this type of modeling is practically indispensable. Given such a company-wide ALM model (ALM = Asset and Liability Model, sometimes also called DFA, Dynamic Financial Analysis), it is natural to want to tweak operations parameters in order to gain an optimal risk exposure. Hence, there is a need to connect the model to an optimization engine. Given the complexity of the model, it is difficult to judge its mathematical properties, such as differentiability, convexity and so on, which are often required for selecting the appropriate optimization algorithm.
The project “Designing for optimal risk exposure” aims to look at the problems associated with practical ALM modeling. Areas of interest are
- optimization algorithms suitable for ALM models
- the specification of an economic environment for the insurance company
- implementation and maintainability issues for the models
- relevant risk measures and
- portfolio theory
Figure 1 The figure shows forecasts of real estate prices and a share index. Both the expected developments as well as one possible scenario are shown. Both assets are modelled using correlated stochastic differential equations and the graph is generated by SimIns, the new simulation platform developed at FCC. This type of models are natural components for describing the asset portfolio of an insurance company.