Panel data modeling of bank deposits
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Panel data modeling of bank deposits Sofia Costa1 · Marta Faias2
· Pedro Júdice3 · Pedro Mota2
Received: 17 December 2019 / Accepted: 26 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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 those models may face failure in an event of crisis. We propose an alternative approach for modeling deposits, using panel data and a momentum term. The model enables the simulation of a variety of deposit trajectories, including episodes of financial distress, showing much 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. Keywords Bank Deposits · Liquidity · Momentum · Panel data JEL Classification C01 · G21 · G32 Mathematics Subject Classification 60G10 · 60G12 · 62J05 · 62M10 · 62P05
1 Introduction The main business of commercial banks is to take deposits and invest in long-term assets (maturity transformation). A bank can invest in liquid or illiquid assets, and
P. Júdice: this research is independent from Montepio Bank and does not reflect the views of this institution.
B
Marta Faias [email protected]
1
Universidade NOVA de Lisboa, Lisbon, Portugal
2
Universidade NOVA de Lisboa and Centro de Matemática e Aplicações (CMA), NOVA, Lisbon, Portugal
3
Montepio Bank and ISCTE Business Research Unit, Lisbon, Portugal
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S. Costa et al.
when there is a deposit run the bank has to sell liquid securities. If there is a shortfall of liquidity, the bank will fail. Thus a bank faces a trade-off between liquid and illiquid holdings in its portfolio. Illiquid assets often are more profitable, but if the institution invests only in those, it will not have liquidity to compensate for unexpected outflows. Therefore, the bank should hold enough cash and securities to face deposit outflows in an event of stress. For example, if in a stress scenario the outflow of deposits is 1 billion euros and the bank only has 500 million euros in liquid assets, it will face failure. The reasoning behind this example is similar to Value at Risk for capital purposes. In the Value at Risk case, the banks should have enough capital to withstand market losses under a stress scenario. For liquidity purposes, the parallel is Liquidity at Risk (LaR): a bank should hold enough liquid assets to withstand deposit outflows under a stress scenario. It is then important to have a good model that simulates deposit dynamics. If a model underestimates liquidi
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