Optimal Capital Structure and Financial Risk of Project Finance Investments Open Access
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By making modifications to existing deterministic and stochastic capital structure decision models, this dissertation addresses three concerns related to the modeling of optimal capital structure for project finance investments: (1) how the nature of coverage ratio constraints affects the decision and its outcome; (2) how to determine the optimal capital structure when a multi-source financing strategy is used; and (3) the value of stochastic modeling in the process. The study addresses these concerns in three papers. The first paper focuses on deterministic optimization models. Here, the study first introduces multi-period constraints for dealing with coverage ratios when debt is homogeneous. It then relaxes the homogeneous debt assumption and considers the situation where debt is heterogeneous. With these modifications, the study examines the flaw of constraining average coverage ratios, and evaluates the effect of a multi-sourced financing strategy on the decision and its outcome. The second paper develops stochastic equivalents of the deterministic models. Here, the study incorporates simulation-based chance constraints to model default risk, when debt is homogeneous and when it is heterogeneous. The modifications in this paper allow the study to assess the importance of chance-constrained programming and the effect of a multi-sourced financing strategy when the capital structure decision is made under uncertainty. In the third paper, the study evaluates the effect of accounting for uncertainty when modeling the capital structure decision; first on the decision and then on project returns and default risk. It achieves this by comparing optimization and simulation results between the deterministic and stochastic models for the two cases of homogeneous and heterogeneous debt. In this regard, the study introduces stochastic dominance and reliability in evaluating the value of stochastic modeling when optimizing capital structure. The study shows that its modifications yield debt financing decisions for which project performance metrics are commensurate with stakeholder requirements and that, there is value in modeling and solving the debt financing decision problem under uncertainty.