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An Integrated CVaR and Real Options Approach to Investments in the Energy Sector

Author

Listed:
  • Fortin, Ines

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

  • Fuss, Sabine

    (University of Maastricht/UNU-Merit, Maastricht, The Netherlands)

  • Hlouskova, Jaroslava

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

  • Khabarov, Nikolay

    (International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria)

  • Obersteiner, Michael

    (International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria)

  • Szolgayova, Jana

    (International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria)

Abstract

The objective of this paper is to combine a real options framework with portfolio optimization techniques and to apply this new framework to investments in the electricity sector. In particular, a real options model is used to assess the adoption decision of particular technologies under uncertainty. These technologies are coal-fired power plants, biomassfired power plants and onshore wind mills, and they are representative of technologies based on fossil fuels, biomass and renewables, respectively. The return distributions resulting from this analysis are then used as an input to a portfolio optimization, where the measure of risk is the Conditional Value-at-Risk (CVaR).

Suggested Citation

  • Fortin, Ines & Fuss, Sabine & Hlouskova, Jaroslava & Khabarov, Nikolay & Obersteiner, Michael & Szolgayova, Jana, 2007. "An Integrated CVaR and Real Options Approach to Investments in the Energy Sector," Economics Series 209, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:209
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    File URL: https://irihs.ihs.ac.at/id/eprint/1770
    File Function: First version, 2007
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    Citations

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    Cited by:

    1. David McInerney & Robert Lempert & Klaus Keller, 2012. "What are robust strategies in the face of uncertain climate threshold responses?," Climatic Change, Springer, vol. 112(3), pages 547-568, June.
    2. Zhang, Mingming & Tang, Yamei & Liu, Liyun & Zhou, Dequn, 2022. "Optimal investment portfolio strategies for power enterprises under multi-policy scenarios of renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Jain, Shashi & Roelofs, Ferry & Oosterlee, Cornelis W., 2014. "Decision-support tool for assessing future nuclear reactor generation portfolios," Energy Economics, Elsevier, vol. 44(C), pages 99-112.
    4. Blyth, William & Bunn, Derek & Chronopoulos, Michail & Munoz, Jose, 2014. "Systematic Analysis of the Evolution of Electricity and Carbon Markets under Deep Decarbonisation," Discussion Papers 2014/39, Norwegian School of Economics, Department of Business and Management Science.
    5. Fuss, Sabine & Szolgayová, Jana & Khabarov, Nikolay & Obersteiner, Michael, 2012. "Renewables and climate change mitigation: Irreversible energy investment under uncertainty and portfolio effects," Energy Policy, Elsevier, vol. 40(C), pages 59-68.
    6. Tietjen, Oliver & Pahle, Michael & Fuss, Sabine, 2016. "Investment risks in power generation: A comparison of fossil fuel and renewable energy dominated markets," Energy Economics, Elsevier, vol. 58(C), pages 174-185.
    7. Shahnazari, Mahdi & McHugh, Adam & Maybee, Bryan & Whale, Jonathan, 2014. "Evaluation of power investment decisions under uncertain carbon policy: A case study for converting coal fired steam turbine to combined cycle gas turbine plants in Australia," Applied Energy, Elsevier, vol. 118(C), pages 271-279.
    8. de Maere d’Aertrycke, Gauthier & Ehrenmann, Andreas & Smeers, Yves, 2017. "Investment with incomplete markets for risk: The need for long-term contracts," Energy Policy, Elsevier, vol. 105(C), pages 571-583.

    More about this item

    Keywords

    Portfolio optimization; CVaR; climate change policy; uncertainty; real options; electricity; investments;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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