Many insurance companies are struggling to overcome the computational challenges involved in computing the Solvency Capital Requirement (SCR) under the Solvency II regime. Standard market approaches, such as least square Monte Carlo and replicating portfolios, can be difficult to calibrate and validate in practice, and also lead to deficient outcomes if not calibrated properly. In this paper, we show how the multilevel Monte Carlo (MLMC) method is a relevant alternative to compute risk-based capital requirements, as it does not rely on any proxy assumptions. We discuss:
- Nested simulations
- The MLMC method
- Adaptive MLMC
- Numerical experiments
- Application to internal models
Efficient computation of Solvency Capital Requirement using Multilevel Monte Carlo Methods
MLMC methods represent a relevant alternative to the proxy modelling approaches commonly used in capital requirement estimation within the insurance sector.