Advanced techniques for optimizing banking liquidity coverage ratio management

Posted: Jul 19, 2018

• Prof. Emma Mehta
• Prof. Emma Zhang
• Prof. Ethan Mendes

Abstract

The liquidity coverage ratio (LCR) represents a critical regulatory requirement for financial institutions worldwide, mandating that banks maintain sufficient high-quality liquid assets to withstand a 30-day stress scenario. Traditional approaches to LCR management have primarily relied on linear programming and conventional optimization techniques that often struggle to capture the complex, multi-dimensional nature of liquidity risk in contemporary financial markets. This research addresses these challenges by developing a novel computational framework that integrates quantum-inspired optimization algorithms with deep reinforcement learning techniques. Our approach represents a significant departure from conventional methodologies by treating LCR optimization as a dynamic, adaptive process rather than a static optimization problem. The primary contributions of this work include the development of a hybrid quantum-classical neural network architecture specifically designed for liquidity optimization, the formulation of a novel reward function that captures both regulatory compliance and operational efficiency objectives, and the demonstration of how innovative algorithmic approaches can transform traditional regulatory compliance practices.