Advanced frameworks for managing interest rate risk in banking investment portfolios

Posted: Feb 22, 2023

• Prof. Isaac Rossi
• Prof. Isabella Ahmed
• Prof. Leo Kowalski

Abstract

The management of interest rate risk represents one of the most critical challenges facing modern banking institutions, particularly in the context of increasingly volatile global financial markets and the complex regulatory landscape established in the post-2008 era. Traditional approaches to interest rate risk management, predominantly centered around duration and convexity measures, have demonstrated significant limitations in capturing the multi-dimensional nature of interest rate movements and their non-linear impact on complex banking portfolios. This research addresses these limitations by introducing a groundbreaking quantum-inspired computational framework that fundamentally reimagines the approach to interest rate risk quantification and management. The novelty of our contribution lies in the integration of quantum computing principles with advanced machine learning techniques, creating a hybrid methodology that transcends the computational boundaries of classical risk management systems. By representing interest rate scenarios as quantum states and employing quantum amplitude estimation to enhance traditional Monte Carlo simulations, we achieve unprecedented computational efficiency while maintaining exceptional accuracy in risk measurement.