A Quantitative Liquidity Model for Banks (PDF)

Chapter 1 Introduction (p. 1-2)

1.1 Motivation

Banks are intermediaries between liquidity supplying depositors and liquidity demanding borrowers.1 Furthermore, they provide contingent liquidity in the form of loan commitments and liquidity backup lines. Importantly, liquidity is a core resource for banks that needs to be actively managed. For that purpose, we will develop a quantitative model of bank liquidity.

Consequently, our model must be stochastic, complete, and will incorporate bank particularities. Here, completeness refers to the fact that the model encompasses product and aggregate as well as short and long-term liquidity. Signi?cantly, an important particularity of banks’ business that our model addresses is con?dence. Incidentally, liquidity modelling is only the starting point for liquidity management, and we therefore discuss modelling, managing and optimizing liquidity. Liquidity does not matter in perfect capital markets2: symmetric information ensures that agents have a perfect knowledge of banks’ asset quality and asset value.

The ability to raise external funds is only limited by the true asset value and not by the value that agents estimate. Moreover, assets are perfectly liquid and can always be sold at their true value. As a consequence, banks are not needed in perfect capital markets. By contrast, the true asset value of banks is unknown to investors in real markets. These investors have to replace the true value with an estimate that could be heavily biased by rumours. Thus, any bank could face funding problems if the bank is exposed to adverse rumours.3 Furthermore, other banks could hoard their liquidity as they face funding dif?culties themselves.

Additionally, liquidity is important for banks since they are the exclusive liquidity channel for central banks. The channel must function effectively to ensure that economy works smoothly. Besides, banks have mutually high liquidity exposures.

Dr. Christian Schmaltz completed his doctoral thesis under the supervision of Prof. Dr. Thomas Heidorn at the Frankfurt School of Finance and Management. He works as a consultant for risk management.