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A Quantitative Liquidity Model for Banks
A Quantitative Liquidity Model for Banks

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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 sop…
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A Quantitative Liquidity Model for Banks Minkšti viršeliai - 20091027

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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.
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