@RISK and RISKOptimizer Help Manage Decisions around Debt Portfolios and Capital Investments

GWU School of Engineering and Applied SciencesWhen faced with multiple kinds of debt instruments or exclusive capital investments, how can an analyst make a decision that maximizes reward but minimizes risk? Emmanuel Donkor, a quantitative analyst and faculty member of the School of Engineering and Applied Sciences (SEAS) at George Washington University addressed these complicated problems, using Palisade software @RISK and RISKOptimizer. His research has led to new and better methods for addressing these financial statistical problems.

Dr. Donkor used Palisade software tools @RISK and RISKOptimizer to conduct two separate research projects published in the journal the Engineering Economist. The first paper (one of the highest ranked articles in the journal) tackled the improvement of debt portfolios for financing capital investment plans, while the other project empirically tested stochastic dominance relationships in risky capital investments.

In his first project, Dr. Donkor, along with Associate Professor Michael Duffey, used @RISK and RISKOptimizer to help analyze and then recommend an appropriate mix of different debt instruments for financing a capital investment project. They first developed a stochastic financial model in Excel, and used RISKOptimizer’s simulation optimization capability to select an optimal mix of fixed-rate debt instruments such that default occurred no more than 5% of the time. They then used @RISK simulation to evaluate the performance of the debt policy prescribed by the optimization model.

In his second project, Dr. Donkor created a spreadsheet framework that uses @RISK to implement empirical tests of stochastic dominance (i.e., ranking random prospects based on preferences regarding outcomes). As a result of Dr. Donkor’s work, analysts can use an @RISK model to empirically test for the best option among many, allowing them to make defensible decisions when comparing risky capital investments, rather than visually-based ‘best guesses’.

» Read the complete case study.

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