Forecasting FX Settlement Liquidity Requirements Using Statistical & ML Models

Abstract

Accurate forecasting of foreign exchange (FX) settlement liquidity requirements remains a critical challenge for global financial institutions operating in an increasingly complex and volatile market environment. This paper presents a comprehensive analysis of statistical and machine learning methodologies applied to predict FX settlement liquidity needs, utilizing data from the period preceding 2022. The global FX market settled daily volumes of USD 6.6 trillion in 2022, with CLS Settlement facilitating 57% of this volume and achieving multilateral netting efficiency of 96%, reducing funding requirements from 100% to just 1% of gross transaction value. Comparative evaluation of seven model architectures—including ARIMA, GARCH, Support Vector Machines, Artificial Neural Networks, Long Short-Term Memory networks, Graph Neural Networks, and ensemble CNN-LSTM models—demonstrates that hybrid approaches achieve prediction accuracy exceeding 97.8% with mean squared error of 0.0125. Advanced machine learning models significantly outperform traditional statistical methods across all forecast horizons, with LSTM-based architectures capturing 91% of volatility dynamics compared to 78% for ARIMA models. This research synthesizes findings on settlement mechanisms, regulatory liquidity requirements under Basel III frameworks, and optimization strategies to enable financial institutions to maintain optimal liquidity buffers while managing counterparty and settlement risks effectively.