Optimal Capital Structure and Financial Risk of Project Finance Investments: A Simulation Optimization Model With Chance Constraints

Abstract

We investigate the use of chance constraints in modeling the debt financing decision under conditions of debt heterogeneity and uncertainty. We develop a stochastic financial model that uses simulation optimization to select an optimal mix of fixed-rate debt instruments from different sources, with the objective of maximizing net present value (NPV) while limiting default risk. We then use simulation to evaluate the performance of the resulting debt policy. Numerical results from our model indicate that in a world of uncertainty, project promoters who wish to create bankable proposals for project financing, by limiting the probability of default, should spread debt across different maturities.

Acknowledgment

Funding for this study was provided by the Logistics Management Institute (LMI) through the Engineering Management and Systems Engineering Department (EMSE) of the School of Engineering and Applied Sciences, George Washington University. We wish to acknowledge the intellectual support received from the informal project review team, composed of selected EMSE faculty and assigned LMI staff.

Notes

The authors can be contacted for the general spreadsheet.

Figure 1 Modules of the model.