An Optimization-Based Approach to the Funding of a Loan Portfolio
Independent thesis Basic level (professional degree)Student thesis
This thesis grew out of a problem encountered by a subsidiary of a Swedish multinational industrial corporation. This subsidiary is responsible for the corporation’s customer financing activities. In the thesis, we refer to these entities as the Division and the Corporation. The Division needed to find a new approach to finance its customer loan portfolio. Risk control and return maximization were important aspects of this need. The objective of this thesis is to devise and implement a method that allows the Division to make optimal funding decisions, given a certain risk limit.
We propose a funding approach based on stochastic programming. Our approach allows the Division’s portfolio manager to minimize the funding costs while hedging against market risk. We employ principal component analysis and Monte Carlo simulation to develop a multicurrency scenario generation model for interest and exchange rates. Market rate scenarios are used as input to three different optimization models. Each of the optimization models presents the optimal funding decision as positions in a unique set of financial instruments. By choosing between the optimization models, the portfolio manager can decide which financial instruments he wants to use to fund the loan portfolio.
To validate our models, we perform empirical tests on historical market data. Our results show that our optimization models have the potential to deliver sound and profitable funding decisions. In particular, we conclude that the utilization of one of our optimization models would have resulted in an increase in the Division’s net income over the past 3.5 years.
Place, publisher, year, edition, pages
Matematiska institutionen , 2004.
Mathematical optimization, systems theory, Financial Optimization, Stochastic Programming, Loan and Lease Portfolio Management, Principal Component Analysis, Monte Carlo Simulation, Multi-Currency Scenario Generation
IdentifiersURN: urn:nbn:se:liu:diva-2664ISRN: LITH-MAT-EX--04/18--SEOAI: oai:DiVA.org:liu-2664DiVA: diva2:20004