Christian Schmaltz

A quantitative liquidity model for banks

liquidity concepts liquidity strategies of banks modelling framework cash flow model liquidity transfer pricing liquidity optimization

Optimal Bank Planning Under Basel III Regulations

We provide a modeling framework for banks’ business planning under Basel III. For this purpose, we write banks’ planning as a formal optimization problem where Basel III minimum requirements/ratios enter as constraints. The linear program provides dual variables that are interpreted as compliance cost for each Basel III ratio. We analyze the effects of Basel III on banks’ product mix for a simplified, deterministic two-product case. In what follows, we generalize the model by incorporating parameter uncertainty, adjustment cost, multiple time steps, and products.

Distance to Compliance Portfolios: An Integrated Shortfall Measure for Basel III

We develop a metric to measure how far banks are away from regulatory compliance under Basel III. Basel III consists of multiple constraints. Our measure is a portfolio that simultanously covers all constraints and their interdependencies. We apply our measure to a sample of European banks and measure how much additional capital, liquid assets and stable funding are needed for compliance. Our methodology can be generalised to measure the distance to any set of linear constraints that organisations have to (regulatory) or want to (internal) comply with. Furthermore, it is a possibility to provide Basel III - impact studies a sound microeconommic basis missing so far.

Optimal Centralization of Liquidity Management

Liquidity is a key resource that banks have to manage on a daily basis. Large banking groups face the question of how to optimally allocate and generate liquidity: in a central liquidity hub or in many decentralized branches across different time zones, jurisdictions, and FX zones. We rephrase the question as a facility-location problem under uncertainty. We show that volatility is a key driver of the degree of (de-)centralization. As expected, in a deterministic setup liquidity should be managed centrally in the most profitable branch. However, under stochastic liquidity demand and different time zones, FX zones, and jurisdictions we find that liquidity is preferably managed and generated in a decentralized fashion to a certain extend. We provide an analytical solution for the 2-branch model. In our setup, a liquidity center is an option on immediate liquidity. Therefore, its value can be interpreted as the “price of information”, i.e. the price of knowing the demand. Furthermore, we derive the threshold to open a liquidity center and show that it is a function of the volatility and the characteristic of the bank network. Finally, we discuss the n-branch model for real-world banking groups (n = 10 to 60 branches) and show that it can be solved with high granularity (100 scenarios) within less than 30 seconds.