The dynamics of liquidity risk is an important issue in what concerns banks’ activity.
It can be approached by studying the evolution of banks’ clients deposits in order to
mitigate the probability of bankruptcy and to efficiently manage banks’ resources. A
sound liquidity risk model is also an important component of any liquidity stress testing
methodology.
In this research1, we aim to develop a model that can help banks to properly manage
their activity, by explaining the evolution of clients deposits throughout time. For this
purpose, we considered the momentum, a frequently used tool in finance that helps to
clarify observed trends. Therefore, we obtained an AR(2) model that was then used to
simulate trajectories, through the use of the R software, for possible evolutions of the
deposits.
Another feature that we pondered was panel data. By considering different banks in
our sample, the simulations would generate varied trajectories, including both good and
bad scenarios, which is useful for stress testing purposes. The mostly referred model in
the literature is the AR(1) model with only one time series, which often does not generate
distress episodes.
In order to validate our model we had to perform several tests, including to the normality
and autocorrelation of the residuals of our model. Furthermore, we considered
the most used model in the literature for comparison with two different individual banks.
We simulated trajectories for all cases and evaluated them through the use of indicators
such as the Maximum Drawdown and density plots.
When simulating trajectories for banks’ deposits, the panel data model gives more
realistic scenarios, including episodes of financial distress, showing much higher drawdowns
and density plots that present a wide range of possible values, corresponding to
booms and financial crises. Therefore, our methodology is more suitable for planning the
management of banks’ resources, as well as for conducting liquidity stress tests
