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The paper gives an overview of the strategies that banks all over the world are currently discussing; focusing on funds transfer pricing (FTP), the active steering of LCR and NSFR, deposit analysis and according strategies, as well as assets and investment products. Finally a structured approach for the strategic analysis and implementation of business changes is briefly outlined.
[Robert Fiedler/Michael Mahlknecht (2013): Basel III: Solving the Liquidity Business Challenge, Cass-Capco Institute Paper Series on Risk, The Capco Institute Journal of Financial Transformation, Journal#37, 04.2013]
If a bank, however wants to be able to manage the LCR not only monthly in retrospective – as mandatory in Basel III – but on an on-going, forward-looking basis, it will need to simulate its future balance sheet. This is already very near to economic risk management techniques.
Although in principal this concept is addressing the problem of a bank’s illiquidity correctly, it is too raw to be used or a bank’s internal liquidity risk management. From the various possible enhancements, we will focus in this article on the bank’s CounterBalancing Capacity, the economically more elaborated version of the HLA. In the LCR there are only three classes of liquifiability of securities: HLA1, HLA2, and the rest which is considered as ‘not liquid’. In practice, an asset’s liquifiability can range from ‘immediately liquifiable’ (e.g. in a central banks refinancing window) to piecewise liquifiability in time with changing haircuts and prices and is also scenario-dependent.
Specifying the liquifiability of each asset separately would be arduous and hardly consistent. To circumvent this, we define an algorithm to assign a number to each individual asset (its Liquifiability Index LiX). The LiX expresses for a pre-defined scenario the asset’s assumed liquifiability as a number, e.g. from zero (completely illiquid) to 100 (best conceivable liquifiability). We will subsequently fine-tune an asset’s LiX to mirror the asset’s specific liquifiability relative to an average asset with the same credit rating. Because the LiX numbers are linearly ordered (0, 1, 2, ... , 99, 100) we can then for practical purposes sort assets with a comparable LiX in liquifiability groups (e.g. from 80 to 90) and assume they have (almost) the same liquifiability in our model. The first is to group securities together that will behave similarly in the chosen scenario.
[Authors: Matthias Küstner, Robert Fiedler and Darren Brooke]
[Quelle: Werner Gleißner/Armin Schaller: Krisendiagnose und Krisenmanagement - Maßnahmenpalette: Von der Ratingprognose bis zur Liquiditätssicherung, in: KSI 04/2009, S. 153-161]
[Quelle: Gleißner, W./Romeike, F.: Analyse Subprime-Krise: Risikoblindheit und Methodikschwächen, in: RISIKO MANAGER 21/2008, S. 1, 8-12.]
[Dissertation zur Erlangung der Würde eines Doktors der Wirtschafts- und Sozialwissenschaften, vorgelegt der Wirtschafts- und sozialwissenschaftlichen Fakultät der Universität Freiburg in der Schweiz]