Integrated Risk Management: Risk Aggregation and Allocation Using Intelligent Systems

In recent years integrated approaches have become state-of-the-art practice for risk management in financial institutions. Contrary to the still common silo-based approach where risk categories and business lines are predominantly analyzed separately, an integrated risk management system adopts an enterprisewide perspective to appropriately account for cross-sectional dependencies between all significant banking risks. In this contribution an application of intelligent systems that provides management with risk-return efficient bank-wide asset allocation strategies is outlined. It is based on multi-objective evolutionary algorithms and considers different market risks and credit risk as well as position volume constraints. The presented novel approach is not only able to integrate the differing goals concerning the risk management function but also to partly overcome the obstacles for risk integration and aggregation. Using real market data a sample portfolio analysis is performed and possible conclusions for a bank risk manager are drawn. The approach is extendable concerning for instance advanced risk measurement methodologies, correlation assumptions, different time horizons and additional risk types. Further real-world constraints, such as regulatory capital, portfolio or P&L restrictions can also be easily integrated into the model.