<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Costa, S.</style></author><author><style face="normal" font="default" size="100%">Faias, M.</style></author><author><style face="normal" font="default" size="100%">Júdice, P.</style></author><author><style face="normal" font="default" size="100%">Mota, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Panel data modeling of bank deposits</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of Finance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s10436-020-00373-1</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">247–264</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 (LaR) methodology. &lt;/p&gt;
&lt;p&gt;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 a novel approach for modeling deposits, using panel data and a momentum term. &lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;p&gt;Therefore, this methodology is more suitable for liquidity management at banks, as well as for conducting liquidity stress tests.&lt;/p&gt;
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