Advanced frameworks for managing interest rate risk in banking balance sheet management

Posted: Aug 17, 2018

• Dr. Prof. Benjamin Chen
• Dr. Prof. Benjamin Mendes
• Dr. Prof. Daniel Park

Abstract

The management of interest rate risk represents one of the most critical challenges facing financial institutions in the contemporary banking landscape. Traditional approaches to interest rate risk management, including duration gap analysis, earnings simulation, and economic value of equity calculations, have served as foundational methodologies for decades. However, these conventional frameworks exhibit significant limitations in capturing the complex, non-linear relationships inherent in modern banking balance sheets, particularly in environments characterized by unprecedented monetary policy interventions and structural shifts in yield curve dynamics. The increasing complexity of financial products, coupled with regulatory requirements under Basel III and IV frameworks, necessitates the development of more sophisticated risk management approaches that can accurately quantify and mitigate interest rate exposures. This research introduces a groundbreaking quantum-inspired computational framework that fundamentally reimagines interest rate risk management in banking institutions. The novelty of our approach lies in its integration of quantum computing principles with traditional financial risk management, creating a hybrid methodology that transcends the computational boundaries of classical algorithms. Unlike conventional methods that rely on simplified assumptions about interest rate behavior and linear approximations of complex financial instruments, our framework leverages quantum amplitude estimation and quantum-inspired optimization techniques to model the full probability distribution of interest rate movements across multiple dimensions of the yield curve. Our research addresses several critical gaps in the existing literature. First, we develop a methodology that captures the non-linear dependencies between various balance sheet components and interest rate scenarios more accurately than traditional duration-based approaches. Second, we introduce a dynamic optimization framework that simultaneously considers regulatory capital requirements, liquidity constraints, and business objectives while managing interest rate risk. Third, we demonstrate how quantum-inspired algorithms can be implemented on classical computing infrastructure, providing practical solutions that financial institutions can deploy immediately while preparing for