Panel data modeling of bank deposits

Studying the dynamics of deposits is important for three reasons: first, it serves as an important component of liquidity stress testing; second, it is crucial to asset-liability management exercises and the allocation between liquid and illiquid assets; third, it is the support for a Liquidity at Risk methodology. Current models are based on AR(1) processes that often underestimate liquidity risk. Thus, a bank relying on thosemodels may face failure in an event of crisis. We propose an alternative approach formodeling deposits, using panel data and amomentum term. The model enables the simulation of a variety of deposit trajectories, including episodes of financial distress, showingmuch higher drawdowns and realistic liquidity at risk estimates, as well as density plots that present a wide range of possible values, corresponding to booms and financial crises. Therefore, this methodology is more suitable for liquidity management at banks, as well as for conducting liquidity stress tests.