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Investing in Time-to-Build Projects With Uncertain Revenues and Costs: A Real Options Approach

Kauppinen, L; Siddiqui, AS; Salo, A; (2018) Investing in Time-to-Build Projects With Uncertain Revenues and Costs: A Real Options Approach. IEEE Transactions on Engineering Management , 65 (3) pp. 448-459. 10.1109/TEM.2018.2803304. Green open access

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Abstract

Lagging public-sector investment in infrastructure and the deregulation of many industries mean that the private sector has to make decisions under multiple sources of uncertainty. We analyze such investment decisions by accounting for both multiple sources of uncertainty and the time-to-build aspect. The latter feature arises in the energy and transportation sectors, because investors can decide the rate at which the project is completed. Furthermore, two explicit sources of uncertainty represent the discounted cash inflows and outflows of the completed project. We use a finite-difference scheme to solve numerically the option value and the optimal investment threshold. Somewhat counterintuitively, with a relatively long time to build, a reduction in the growth rate of the discounted operating cost may actually lower the investment threshold. This is contrary to the outcome when the stepwise aspect is ignored in a model with uncertain price and cost. Hence, research and development efforts to enhance emerging technologies may be more relevant for infrastructure projects with long lead times.

Type: Article
Title: Investing in Time-to-Build Projects With Uncertain Revenues and Costs: A Real Options Approach
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TEM.2018.2803304
Publisher version: http://doi.org/10.1109/TEM.2018.2803304
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Investment, Uncertainty, Production facilities, Research and development, Economic indicators, System analysis and design
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10045735
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