Investment evaluation in renewable projects under uncertainty, using real options analysis: the case of wind power industry
-
Published March 31, 2017
- Author(s)
-
DOIhttp://dx.doi.org/10.21511/imfi.14(1).2017.10
-
Article InfoVolume 14 2017, Issue #1, pp. 96-103
- TO CITE АНОТАЦІЯ
-
Cited by4 articlesJournal title: Problems and Perspectives in ManagementArticle title: Application of the methodology for determining the “growth poles” of the region’s industrial economy in the system of public administrationDOI: 10.21511/ppm.15(4).2017.07Volume: 15 / Issue: 4 / First page: 72 / Year: 2017Contributors: Nadiia PysarJournal title: Investment Management and Financial InnovationsArticle title: Simulative model for evaluation of investment processes in the regions of UkraineDOI: 10.21511/imfi.14(3-2).2017.03Volume: 14 / Issue: 3 / First page: 322 / Year: 2017Contributors: Ivan Blahun, Lesia Dmytryshyn, Halyna LeshukJournal title: Journal of Environmental ManagementArticle title: Uncertainty analysis of industrial energy conservation management in China's iron and steel industryDOI: 10.1016/j.jenvman.2018.07.096Volume: 225 / Issue: / First page: 205 / Year: 2018Contributors: Zongguo Wen, Yihan Wang, Chenkai Zhang, Xiaoling ZhangJournal title: Investment Management and Financial InnovationsArticle title: Economic security in investment projects management: convergence of accounting mechanismsDOI: 10.21511/imfi.14(3-2).2017.06Volume: 14 / Issue: 3 / First page: 353 / Year: 2017Contributors: Nataliia Ostapiuk, Oleksandra Karmaza, Mykola Kurylo, Gennady Timchenko
- 1610 Views
-
1159 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Investment analysis is a crucial process for any investment’s success. This process can be supported by both the discounted cash flow analysis and the real options analysis. Many researchers have point out restrictions for the first one, in cases of uncertainty in the entrepreneurial environment. The main types of uncertainty, concerning the wind energy sector, include uncertainties related to the price of electriticity by RES, the public policy regulatory policies, the demand, the initial capital costs, the technological progress, the weather conditions, the political and economical situations and generally the RES market structure. In this paper, we try to find the optimal investment strategy in a liberalized global electricity market, where the price of electricity is uncertain while the other parameters are configured separately in each country. The authors consider about the factors of the time for investment and the electricity’s price level, in wind energy by using the real options theory. The authors select a variety of data for the wind energy industry from different countries in several continents, and also create a model for the investment analysis in this entrepreneurial sector.
- Keywords
-
JEL Classification (Paper profile tab)M21
-
References31
-
Tables3
-
Figures2
-
- Fig. 1. Annual installed capacity by region 2007-2015
- Fig. 2. Market forecast for 2016-2020
-
- Table 1. Public Support Mechanisms
- Table 2. Interest rates
- Table 3. Investment evaluation
-
- Altug, S., Demers, F., and Demers, M. (2001). The impact of Tax Risk and Persistence on Investment Decisions. Economics Bulletin, 5, 1-6.
- Auer, H. (2008). Overview of the main RES – E support schemes for wind energy in the EU-27 Member States, September 2008.
- Blanco, M. (2009). The economics of wind energy. Renewable and Sustainable Energy Reviews, 13(6-7), 1372-1382.
- Brekke, K. A., Schieldrop, B. (1999). Investment in flexible technologies under uncertainty. In M.J.Brennan and L. Trigeorgis, eds., Project Flexibility, Agency and Competition: New Developments in the Theory of Real Options (pp. 34-49), Oxford University Press, New York.
- Davis, G., Owens, B. (2003). Optimizing the level of renewable electric R and D expenditures using real options analysis. Energy Policy, 31(15), 229-263.
- Deng, S-J., Oren, S. S. (2003). Incorporating operational characteristics and startup costs in option-based valuation of power generation capacity. Probability in the Engineering and Information Sciences, 17(2), 155-182.
- EWEA (2016). Wind in power 2015 – European statistics. THE EUROPEAN WIND ENERGY ASSOCIATION, February.
- GWEC (2015). GLOBAL WIND REPORT – ANNUAL MARKET UPDATE. Global Wind Energy Council – GWEC.
- Hassett, K. A., Metcalf, G. E. (1995). Investment under alternative return assumptions: Comparing random walks and mean reversion. Journal of Economic Dynamics and Control, 19, 1471-1488.
- Hassett, K. A., Metcalf, G. E. (1999). Investment with uncertain tax policy: Does Random Tax Policy discourages investment? The Economic Journal, 109, 372-393.
- Herbelot, O. (1992). Option Valuation of Flexible Investments: The case of environmental investments in the electric power industry. MIT Press, PhD Thesis.
- IEA (2011). IEA WIND2011 Annual Report, July 2012.
- Kjærland, F. (2007). A real option analysis of investments in hydropower: The case of Norway. Energy Policy, 35, 5901-5908.
- Kinias I. (2015). Renewable Energy Sources. Europe vs BRICS, 16th International Conference of the Global Academy of Business & Economic Research (GABER), 22-42, New York, USA.
- Kobila, T. (1990). The choice between hydro and thermal power generation under uncertainty. O. Olsen and J. Vislie, eds., Recent modeling approaches in applied energy economics, International Studies in Economic.
- Madlener, R., Kumbaroglu, G., Ediger, V. S. (2005). Modeling technology adoption as an irreversible investment under uncertainty: the case of the Turkish electricity supply industry, Energy Economics, 27, 139-163.
- Moreira, A., Rocha, K., David, P. (2004). Thermopower generation investment in Brazil-economic conditions, Energy Policy, 32(1), 91-100.
- Murto, P. (2003). Timing of investment under technological and revenue related uncertainties. Helsinki University of technology, E11.
- Myers, S. C. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5(2), 147-175.
- OECD (2016). Annual Statistics 2016.
- Panteghini, P. M., Scarpa, C. (2003). Irreversible Investments and Regulatory Risk. CES Working Paper 934.
- Renewable Energy Roadmap (2016). Roadmap for a Renewable Energy Future, International Renewable Energy Agency, 2016 Edition.
- Salahor, G. (1998). Implications of outputs price risk and operating leverage for the evaluation of petroleum development projects. The Energy Journal, 19(1), 13-46.
- Sarkar, S. (2003). The effect of mean reversion on investment under uncertainty, Journal of Economic Dynamics and Control, 28, 377-396.
- Schwartz, E. (1997). The stochastic behavior of commodity prices: implications for valuation and hedging. Journal of Finance, 52, 923-973.
- Seifert, J., Marliese Uhrig – Homburg (2007). Modeling jumps in electricity prices: theory and empirical evidence. Review of Derivatives, 10(1), 59-85.
- Smith, J., McCardle, K. (1999). Options in the real world: lessons learned in evaluating oil and gas investments, Operations Research, 47(1), 1-15.
- Tseng, C., Barz, G. (2002). Short – term generation asset valuation: a real options approach. Operations Research, 50(2), 297-310.
- Yeo, K. T., Qui, F. (2003). The value of management flexibility – a real option approach to investment evaluation. International Journal of Project Management, 21, 243-250.
- Venetsanos, K., Angelopoulou, P., Tsoutsos, T. (2002). Renewable Energy Sources project appraisal under uncertainty: the case of wind energy exploitation within a changing energy market development. Energy Policy, 30, 297-307.
- Verra, A. (2009). Modeling the income uncertainty using the real options theory: the case of renewable energy industry. PhD Thesis, UoA.
-
Valuing synergies in strategic mergers and acquisitions using the real options approach
Anna Loukianova , Egor Nikulin , Andrey Vedernikov doi: http://dx.doi.org/10.21511/imfi.14(1-1).2017.10Investment Management and Financial Innovations Volume 14, 2017 Issue #1 (cont.) pp. 236-247 Views: 3072 Downloads: 2862 TO CITE АНОТАЦІЯThe purpose of the current paper is to elaborate the model for assessing cumulative synergetic effect in M&A (Mergers and Acquisitions) deals on the basis of a real options approach. The majority of papers on the synergetic effects of M&A deals typically focus on a particular type of synergy, while the current paper proposes a model that accounts for the cumulative simultaneous effect of different types of operating and financial synergies. The methodology of our research is loosely based on Datar-Mathews real option valuation model, which is flexible and intuitive for practitioners. Formulae for assessing eight types of synergy typically arising from M&A deals are developed. They are integrated into a single model to assess their cumulative effect on the M&A deal using a simulation modelling approach. The method was used ex post to find synergy values in two recent M&A deals in the pharmaceutical industry, and produced sound results. The proposed approach to value target companies could be used by firms before an M&A deal in the due diligence process. Using this tool a company can build a bidding strategy and define the maximum premium it can pay for the target.
-
A model for analyzing the financial stability of banks in the VUCA-world conditions
Svitlana Khalatur, Liudmyla Velychko
, Olena Pavlenko
, Oleksandr Karamushka
, Mariia Huba doi: http://dx.doi.org/10.21511/bbs.16(1).2021.16
Banks and Bank Systems Volume 16, 2021 Issue #1 pp. 182-194 Views: 2978 Downloads: 796 TO CITE АНОТАЦІЯVUСA is a chaotic and rapidly changing business environment that, based on the variability, uncertainty, complexity and ambiguity of the modern world, transforms the approach of banks to the analysis of financial stability. The aim of the paper is to improve tools for monitoring the impact of VUCA-world conditions on the financial stability of banks, namely a model for studying and analyzing the impact of the modern business space “VUCA” on the financial stability of the country's banks. To test the model, the method of constructing regression equations in multifactor regression analysis is used. For this study, data from some Eastern European countries (Ukraine, Belarus, Latvia, Lithuania, Moldova) were used, and time series data were used for 10 years from 2010 to 2019.
Having considered the definition of “VUCA-world conditions”, the model of modern business space “VUCA” was developed when analyzing the activity of banks in the studied countries. Drivers, consequences, requirements and macroeconomic indicators of the countries’ activities in the VUСA-world conditions are determined. The VUCA-world conditions also consider the study of key macroeconomic indicators that allow building long-term relationships throughout the value chain. The analysis of the studied Eastern European countries showed that with the increase of factors of GDP growth, GNI per capita growth, research and development costs, foreign direct investment, and net inflow of 1%, the effective ratio of bank capital and assets also increases. The assessment, in contrast to the existing ones, makes it possible to consider the impact of the macroeconomic environment of banks on their financial stability. -
Loan restructuring as a banking solution in the COVID-19 pandemic: Based on contingency theory
I Gusti Ayu Eka Damayanthi, Ni Luh Putu Wiagustini
, I Wayan Suartana
, Henny Rahyuda
doi: http://dx.doi.org/10.21511/bbs.17(1).2022.17
Banks and Bank Systems Volume 17, 2022 Issue #1 pp. 196-206 Views: 1790 Downloads: 990 TO CITE АНОТАЦІЯThe world’s economic growth has decreased due to the COVID-19 pandemic. Many companies are experiencing financial distress, so they cannot pay off their maturing debts. Banks as lenders face the risk of non-performing loans. The increasing number of unpaid loans will reduce a bank’s operating income and gain. The contingency approach is used as a conditional factor that can increase the effectiveness of firm performance. The relevance of this study is how banking strategies overcome the problem of uncertainty regarding risk and return during a pandemic. Contingency theory describes organizational success as influenced by contextual factors and established strategies. The purpose of this study is to systematically review the literature related to loan restructuring as a solution to non-performing loans in banking companies in Indonesia. The research method is a review of 40 articles from Scopus and a descriptive analysis of company financial statement notes to see what strategies banks are using during the COVID-19 pandemic. Based on contingency theory, the results of the study explain organizational success which is influenced by contextual factors and the established strategy. The more appropriate the strategy chosen in a given situation, the higher the achievement of organizational performance. A qualitative analysis provides a solution for a bank to overcome the problem of unpaid loans at maturity through a restructuring model strategy with modified loan terms.