IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1225-d1350871.html
   My bibliography  Save this article

Real Options Volatility Surface for Valuing Renewable Energy Projects

Author

Listed:
  • Rosa-Isabel González-Muñoz

    (School of Management, Universidad de los Andes, Bogota 111711, Colombia)

  • Jesús Molina-Muñoz

    (School of Management, Universidad del Rosario, Bogota 111221, Colombia)

  • Andrés Mora-Valencia

    (School of Management, Universidad de los Andes, Bogota 111711, Colombia)

  • Javier Perote

    (Department of Economics and Economic History and IME, Campus Miguel de Unamuno, University of Salamanca, 37007 Salamanca, Spain)

Abstract

Real options analysis is an adequate tool with which to value companies and projects under investment uncertainty. Nevertheless, the estimation of the volatility to be employed in the valuation procedure is a challenging task. The volatility parameter not only affects the investment value, but is also important in strategic decision-making. The aim of this paper is to provide a suitable methodology for the estimation of volatility in real option project valuation, with a focus on renewable energy projects. Our procedure is a straightforward extension of the implied volatility methodology employed for financial options; however, our proposal considers the debt-to-equity ratio instead of the moneyness or strike price. Thus, the volatility of the project is the implied volatility obtained from the volatility surface of comparable firms for a certain valuation date and the given debt-to-equity relation of a renewable project. Furthermore, the natural spline model is utilized to calibrate the volatility surface for real option valuation purposes. The empirical results demonstrate that the implied volatility ranges from 3.37% to 113.78%, with median values between 16.42% and 47.10%, in the period from January 2014 to December 2020, for our research study. Finally, we consider that our proposal is a natural and straightforward manner in which to estimate the implied volatility for projects under investment uncertainty, since real option valuation is based on the same idea and tools used in financial option pricing.

Suggested Citation

  • Rosa-Isabel González-Muñoz & Jesús Molina-Muñoz & Andrés Mora-Valencia & Javier Perote, 2024. "Real Options Volatility Surface for Valuing Renewable Energy Projects," Energies, MDPI, vol. 17(5), pages 1-13, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1225-:d:1350871
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1225/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/5/1225/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kroniger, Daniel & Madlener, Reinhard, 2014. "Hydrogen storage for wind parks: A real options evaluation for an optimal investment in more flexibility," Applied Energy, Elsevier, vol. 136(C), pages 931-946.
    2. Pedro Godinho, 2006. "Monte Carlo Estimation of Project Volatility for Real Options Analysis," GEMF Working Papers 2006-01, GEMF, Faculty of Economics, University of Coimbra.
    3. E. Brandão, Luiz & Dyer, James S. & Hahn, Warren J., 2012. "Volatility estimation for stochastic project value models," European Journal of Operational Research, Elsevier, vol. 220(3), pages 642-648.
    4. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    5. Das Gupta, Supratim, 2021. "Using real options to value capacity additions and investment expenditures in renewable energies in India," Energy Policy, Elsevier, vol. 148(PA).
    6. Elder, John & Serletis, Apostolos, 2011. "Volatility In Oil Prices And Manufacturing Activity: An Investigation Of Real Options," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 379-395, November.
    7. Doumpos, Michael & Niklis, Dimitrios & Zopounidis, Constantin & Andriosopoulos, Kostas, 2015. "Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 599-607.
    8. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    9. Robert McDonald & Daniel Siegel, 1986. "The Value of Waiting to Invest," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 101(4), pages 707-727.
    10. Maria Vassalou & Yuhang Xing, 2004. "Default Risk in Equity Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 831-868, April.
    11. Pedro Godinho, 2018. "Simulation-based estimation of state-dependent project volatility," The Engineering Economist, Taylor & Francis Journals, vol. 63(3), pages 188-216, July.
    12. Oliver Levine & Youchang Wu, 2021. "Asset Volatility and Capital Structure: Evidence from Corporate Mergers," Management Science, INFORMS, vol. 67(5), pages 2773-2798, May.
    13. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    14. Amaya, Diego & Boudreault, Mathieu & McLeish, Don L., 2019. "Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 297-313.
    15. Afik, Zvika & Arad, Ohad & Galil, Koresh, 2016. "Using Merton model for default prediction: An empirical assessment of selected alternatives," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 43-67.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sim, Jaehun & Kim, Chae-Soo, 2019. "The value of renewable energy research and development investments with default consideration," Renewable Energy, Elsevier, vol. 143(C), pages 530-539.
    2. Carlos Andrés Zapata Quimbayo, 2020. "OPCIONES REALES Una guía teórico-práctica para la valoración de inversiones bajo incertidumbre mediante modelos en tiempo discreto y simulación de Monte Carlo," Books, Universidad Externado de Colombia, Facultad de Finanzas, Gobierno y Relaciones Internacionales, number 138, August.
    3. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.
    4. Pryshchepa, Oksana & Aretz, Kevin & Banerjee, Shantanu, 2013. "Can investors restrict managerial behavior in distressed firms?," Journal of Corporate Finance, Elsevier, vol. 23(C), pages 222-239.
    5. Dibooglu, Sel & Cevik, Emrah I. & Tamimi, Hussein A. Hassan Al, 2022. "Credit default risk in Islamic and conventional banks: Evidence from a GARCH option pricing model," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 396-411.
    6. Turalay Kenc & Emrah Ismail Cevik, 2021. "Estimating volatility clustering and variance risk premium effects on bank default indicators," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1373-1392, November.
    7. Miranda, Oscar & Brandão, Luiz E. & Lazo Lazo, Juan, 2017. "A dynamic model for valuing flexible mining exploration projects under uncertainty," Resources Policy, Elsevier, vol. 52(C), pages 393-404.
    8. Amaya, Diego & Boudreault, Mathieu & McLeish, Don L., 2019. "Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 297-313.
    9. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    10. Turalay Kenc & Emrah Ismail Cevik & Sel Dibooglu, 2021. "Bank default indicators with volatility clustering," Annals of Finance, Springer, vol. 17(1), pages 127-151, March.
    11. Hu, Xiaolu & Shi, Jing & Wang, Lafang & Yu, Jing, 2020. "Foreign ownership in Chinese credit ratings industry: Information revelation or certification?," Journal of Banking & Finance, Elsevier, vol. 118(C).
    12. Seiji Harikae & James S. Dyer & Tianyang Wang, 2021. "Valuing Real Options in the Volatile Real World," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 171-189, January.
    13. Huang, Hsing-Hua & Lee, Han-Hsing, 2013. "Product market competition and credit risk," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 324-340.
    14. Kaido Kepp & Kadri Männasoo, 2021. "Investment irreversibility and cyclical adversity: Implications for the financial performance of European manufacturing companies," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(7), pages 1665-1678, October.
    15. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    16. Wu, Ji & Yao, Yao & Chen, Minghua & Jeon, Bang Nam, 2020. "Economic uncertainty and bank risk: Evidence from emerging economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 68(C).
    17. Moon, Yongma & Baran, Mesut, 2018. "Economic analysis of a residential PV system from the timing perspective: A real option model," Renewable Energy, Elsevier, vol. 125(C), pages 783-795.
    18. Tao, Qizhi & Chen, Carl & Lu, Rui & Zhang, Ting, 2017. "Underfunding or distress? An analysis of corporate pension underfunding and the cross-section of expected stock returns," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 116-133.
    19. Xia, Han, 2014. "Can investor-paid credit rating agencies improve the information quality of issuer-paid rating agencies?," Journal of Financial Economics, Elsevier, vol. 111(2), pages 450-468.
    20. Hui Chen & Jianjun Miao & Neng Wang, 2010. "Entrepreneurial Finance and Nondiversifiable Risk," The Review of Financial Studies, Society for Financial Studies, vol. 23(12), pages 4348-4388, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1225-:d:1350871. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.