More efficient interest rate markets through machine learning

Important results from the project

The purpose has been to study how machine learning methods can be used to improve the management of interest rate risks. By formulating optimization models that improve the measurement of interest rate curves, the estimation of stochastic processes and determine optimal decisions, significant improvements have been obtained, relatively traditional hedging of interest rate risks.

Expected long term effects

By making existing optimization models more realistic through better handling of noise in market data, identification of affordable assets and transaction costs, the expectation was that risk management would be more efficient. When validating with historical data, significant improvements can be observed. Via performance attribution, the improvement can be traced to a more cost-effective hedge being identified where the risk exposure is limited.

Approach and implementation

In collaboration with Handelsbanken, SEB and Swedbank, the models have been validated and developed to become more realistic. Optimization provides both the tool to identify improvements, but also to validate that the improvements are obtained when they are applied in practice. Through a systematic operations research approach, the uncertainty that exists in real problems can be managed, in order to identify optimization models that really work better in practice.