Balance sheet management solution: integrating credit, interest and liquidity cost

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dc.contributor.authorStrydom, Petrus Johannes
dc.date.accessioned2018-10-22T13:02:38Z
dc.date.available2018-10-22T13:02:38Z
dc.date.issued2018
dc.descriptionDegree of Doctor of Philosophy: A Thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the Doctor of Philosophy. May 29, 2018en_ZA
dc.description.abstractThis thesis main contribution is the model framework based on the projection of the banks' balance sheet taking all major nancial risks into account. Real bank data is used to support this analysis, covering a large range of customer and product level data across both retail and corporate products. This thesis explicitly explores the link between retail customers from a deposit, lending activity and performance and other market driven factors such as liquidity risk premium of cost of funding. Various optimization techniques are tested, con rming the value of a strategy that dynamically re-balance the funding pro le of the bank versus a more static approach. In this thesis, we apply two optimisation frameworks to determine the optimal wholesale funding mix of a bank, given uncertainty in both credit and liquidity risk. A stochastic linear programming method is used to nd the optimal strategy to be maintained across all scenarios. A recursive learning method is developed to provide the bank with a trading signal to dynamically adjust the wholesale funding mix as the macroeconomic environment changes. The performance of the two methodologies is compared in chapter 3. The optimisation target is the net interest income of the bank. The on-line recursive learning method provides superior results as this allows the bank to dynamically adjust the funding pro le. This thesis integrates the sub-components underlying the bank's balance sheet to facilitate the projection of the net interest income allowing for both liquidity, interest and credit risk. The sub-components include retail and wholesale loans, retail and wholesale deposits and bank issued debt instruments. Actual historical data was obtained from a South African bank to calibrate a model for each of these subcomponents (discussed in chapters 4-7).en_ZA
dc.description.librarianMT 2018en_ZA
dc.identifier.urihttps://hdl.handle.net/10539/25873
dc.language.isoenen_ZA
dc.titleBalance sheet management solution: integrating credit, interest and liquidity costen_ZA
dc.typeThesisen_ZA
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