Banks’ liquidity management dynamics: evidence from Indonesia

Abstract

PurposeThis study aims to cover an important yet largely under-explored topic: the dynamic process of bank liquidity management in a vast developing economy by considering pool of funds hypothesis, signaling hypothesis and risk management hypothesis.Design/methodology/approachThe authors apply the dynamic common correlated effect (DCCE) method with an error correction model format to a long panel datasets of 84 Indonesian banks from January 2003 to August 2019, resulting in 16,800 observations.FindingsThe authors obtain convincing evidence of dynamic liquidity management with an error correction mechanism. The time needed to adjust to a liquidity shock ranges from 2.5 to 3.5 months. The empirical results strongly support the pool of funds and signaling hypotheses, whereas risk management motive appears to have secondary importance.Practical implicationsThe regulator should also encourage banks to diversify liquidity management to include interbank money market and off-balance-sheet instruments. The current condition shows that bank liquidity management is strongly correlated with intermediation dynamics and thus is contracyclical. Banks could end up with tight liquidity in a booming economy, which would pose a severe risk to their financial standing.Originality/valueTo authors’ knowledge, this study is the first to analyze bank liquidity management behavior empirically using a panel error correction mechanism. Here, the authors also try to combine a practitioner perspective with a scientific one.