A Quantitative Liquidity Model for Banks
1st Edition
Published on 30. May 2010
XXIII, 223 pages
978-3-8349-8554-5 (ISBN)
Description
Liquidity is a core resource and its management is a core activity of banks. Nevertheless, liquidity management has not received much attention during the last decades, as liquidity has not been perceived as scarce. This perception has clearly changed during the ?nancial crisis 2007/2009. Facing dried interbank markets, many banks were desperately looking for liquidity. Despite its crucial role, the modeling techniques for bank liquidity are so far rather simple, which sharply contrasts the sophisticated techniques used for other risks as credit or market. Furthermore, German regulators now allow banks to use internal liquidity models for regulatory reporting. This leads to the need to develop a liquidity model for banks that uses advanced stochastic techniques, incorporates all liquidity key variables, discusses internal liquidity allocation and optimization. The work of Christian Schmaltz closes this gap in the literature. There are three major contributions: 1. Key liquidity variables are derived. 2. An innovative way to internally allocate liquidity is developed. 3. Transfer prices of liquidity are calculated. The key variables are derived from the liquidity condition of banks and the channels to generate additional cash ?ows. Customer deposits and credit, funding spread and fu- ing capacity, haircuts and short term interest rates are identi?ed as key liquidity variables. Liquidity risk is the consequence of the non-deterministic nature of these variables, which may take large adverse values (liquidity crisis). Having identi?ed the key variables, a l- uidity model is set up by assuming a particular stochastic process for each variable.
More details
Series
Language
English
Place of publication
Wiesbaden
Germany
Publishing group
Betriebswirtschaftlicher Verlag Gabler
Target group
Professional and scholarly
Illustrations
XXIII, 223 p.
File size
3,66 MB
ISBN-13
978-3-8349-8554-5 (9783834985545)
DOI
10.1007/978-3-8349-8554-5
Schweitzer Classification
Other editions
Additional editions
Christian Schmaltz
A Quantitative Liquidity Model for Banks
Book
10/2009
1st Edition
Springer Gabler
€53.49
Shipment within 7-9 days
Person
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.
Content
Liquidity Concepts.- Liquidity Framework.- Liquidity Model.- Liquidity Management.- Liquidity Optimization.- Conclusion.
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. The failure of one bank can easily encroach on other banks. Finally, liquidity is for banks what commodities are for corporations: an input factor for their (loan) production function. Hence, liquidity is important for banks in general. Institutional changes during the last decade require a readjustment of banks' liquidity management. Important changes are.
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. The failure of one bank can easily encroach on other banks. Finally, liquidity is for banks what commodities are for corporations: an input factor for their (loan) production function. Hence, liquidity is important for banks in general. Institutional changes during the last decade require a readjustment of banks' liquidity management. Important changes are.
