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Measuring infrastructure investment option value

Listed author(s):
  • Gabriel J Power
  • Charli D. Tandja M.
  • Josée Bastien
  • Philippe Grégoire

Purpose - – The purpose of this paper is to propose a risk-based framework to estimate the option value of infrastructure investment, accounting for the stochastic behavior of both financial and physical (engineering) variables. Design/methodology/approach - – This study uses a real-options approach and computes the optimal investment dates and option values using Least Squares Monte Carlo, both the original Longstaff – Schwartz algorithm and the constrained Least Squares approach of Le tourneau – Stentoft. Findings - – Real-option value for infrastructure investment is substantial. It is beneficial to model jointly financial and engineering risks to better understand the timing and real-option value of infrastructure investment. The analysis further shows which variables are option value drivers. Research limitations/implications - – Future work could integrate financing constraints into the model, consider path dependency in the physical state variables or integrate sovereign risk, expropriation risk, operational risk or other project risks. Practical implications - – Financial practitioners and investment managers interested in infrastructure risk finance or project finance will benefit from a novel framework to analyze infrastructure investments in which engineering and financial risks interact in a tractable way. Social implications - – Public decision-makers will benefit from a better understanding of what determines the value of infrastructure investments, how real-option value affects optimal investment timing and how both are determined by financial and engineering risks. Originality/value - – The analysis considers financial and engineering risks in a single framework to better understand option value in infrastructure investment. The framework and findings are useful both to risk finance and project finance practitioners and investors as well as engineers and public sector decision-makers.

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Article provided by Emerald Group Publishing in its journal The Journal of Risk Finance.

Volume (Year): 16 (2015)
Issue (Month): 1 (January)
Pages: 49-72

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Handle: RePEc:eme:jrfpps:v:16:y:2015:i:1:p:49-72
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