Multi-Horizon Forecasting of Central Bank Liquidity Using Deep Learning Models: Assessing the Impact of Exogenous Factors

Abstract

In emerging, hydrocarbon-dependent economies like Algeria, managing banking liquidity is a persistent challenge due to external shocks and structural breaks. The Bank of Algeria (BoA) must accurately forecast aggregate liquidity to calibrate open market operations, set reserve requirements, and safeguard financial stability. Traditional statistical models, such as ARIMA, SARIMA, and SARIMAX, have long served as the backbone of central bank liquidity forecasting due to their transparency and ability to capture seasonal patterns. However, these linear approaches frequently struggle with the non-linear dynamics and complex interactions characteristic of Algerian liquidity data, particularly during periods of external shocks like the 2014 oil crisis and the COVID-19 pandemic. This dissertation addresses this gap by comparing the performance of two traditional models (SARIMA and SARIMAX) against three deep learning architectures (CNN, LSTM, and GRU) in forecasting aggregate central bank liquidity for the Bank of Algeria. The study evaluates forecasting accuracy across three operational horizons—day-ahead, week-ahead, and month-ahead—and assesses the marginal value of incorporating exogenous variables (Brent crude oil price, USD/DZD exchange rate, and holiday/seasonal dummies). The findings confirm that deep learning models, particularly LSTM and GRU, significantly outperform traditional statistical models by better capturing non-linear dynamics and structural breaks. Furthermore, the inclusion of exogenous variables related to hydrocarbon prices and exchange rates substantially reduces forecasting errors. Ultimately, this study provides both a methodological contribution to the literature and practical, operational guidance for the Bank of Algeria’s transition toward proactive, AI-driven liquidity management.