Impact of specific liquidity shocks on the bank's solvency

Purpose - This study aims to demonstrate and measure the impact of liquidity shocks on a bank’s solvency, especially when the bank does not hold sufficient liquid assets. Design/methodology/approach - The proposed model is an extension of Merton’s (1974) model. It assesses the bank’s probability of default over one or two (short) periods relative to liquidity shocks. The shock scenarios are materialised by different net demands for the withdrawal of funds (NDWF) and may lead the bank to sell illiquid assets at a depreciated value. We consider the possibility of second-round effects at the beginning of the second period by introducing the probability of their occurrence. This probability depends on the proportion of illiquid assets put up for sale following the initial shock in different dependency scenarios. Findings - We observe a positive relationship between the initial NDWF and the bank’s probability of default (particularly over the second period, which is conditional on the second-round effects). However, this relationship is not linear, and a significant proportion of liquid assets makes it possible to attenuate or even eliminate the effects of shock scenarios on bank solvency. Practical implications - The proposed model enables banks to determine the necessary level of liquid assets, allowing them to resist (i.e. remain solvent) different liquidity shock scenarios for both periods (including eventual second-round effects) under the assumptions considered. Therefore, it can contribute to complementing or improving current internal liquidity adequacy assessment processes (ILAAPs). Originality/value - The proposed microprudential approach consists of measuring the impact of liquidity risk on a bank’s solvency, complementing the current prudential framework in which these two topics are treated separately. It also complements the existing literature, in which the impact of liquidity risk on solvency risk has not been sufficiently studied. Finally, our model allows banks to manage liquidity using a solvency approach.