An Optimization Approach to Continuous Liability Management

Since the 70's both the volatility and level of interest rates have risen. This has lead to an increase in companies' interest rate risks. A stable income source is no longer a guarantee for financial success. To cope with this problem a more active portfolio management has to be employed. Many tools used in liability management, like interest rate models, use historical data in order to describe the behavior of the market. This implies that a massive amount of financial data needs to be processed to enable sound decision making. Without the help of computers, this problem is difficult for humans to handle. Using optimization algorithms, many different parameters can be analyzed at the same time. This thesis uses an optimization approach to solve the liability management problem. A method including liquidity risk and interest rate risk is developed based on the concept of linear programming. The usefulness of the method is investigated, using an implementation incorporating the expectations hypothesis for interest rate forecasting and a GARCH model for volatility forecasting. The method developed in this thesis appears to be efficient in handling the large amount of data. The output from the method can be used as a sound recommendation if satisfactory interest rate forecasts are available. The expectations hypothesis though fails to meet this demand and should be replaced with other, more developed methods