Advanced frameworks for managing interest rate risk in banking investment portfolios

Posted: Dec 10, 2019

• Prof. Liam Rossi
• Prof. Lucas M¨ uller
• Prof. Lucas Petrova

Abstract

The management of interest rate risk represents one of the most critical challenges facing modern banking institutions, particularly in the context of investment portfolio management. Traditional approaches to interest rate risk management, primarily centered around duration and convexity measures, have demonstrated significant limitations in capturing the complex, non-linear dynamics of contemporary financial markets. This research addresses these challenges by introducing a groundbreaking quantum-inspired computational framework that fundamentally reimagines how banking institutions approach interest rate risk management. Drawing inspiration from quantum computing principles, our methodology represents interest rate scenarios as quantum probability amplitudes, enabling simultaneous evaluation of multiple risk pathways and capturing complex correlation structures that traditional models overlook. The framework integrates machine learning techniques with quantum computational concepts to create a hybrid system that offers superior predictive accuracy and risk assessment capabilities compared to existing approaches.