Pedro Mota

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.

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.

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.

Nowadays, there are still countless researchers defending the effectiveness of the moving average technical analysis and they are able to present evidences for certain stocks, indexes and/or markets where this technical indicator is extremely useful for defining trading strategies. But the contrary also exists, i.e. a lot of researchers show distrust of this technical indicator and also provide evidences with particular stocks, indexes and/or markets where moving averages based strategies do not work well.
Aiming to understand why is it that with some stocks the moving average is indeed an excellent indicator while with others it is not, in this paper we implement moving average based strategies to buy and/or sell stocks for more than 480 companies from the NASDAQ 100, FTSE 100 and SP 500 indexes and compare the results with the ones obtained when using the buy-and-hold strategy.