Author
Abstract
This paper studies several risks involved in financing wind energy projects and alternatives for financing the projects. Since the projects have high initial costs, they need to be leveraged heavily typically in project finance transaction. The paper includes an empirical analysis of major wind turbine manufacturers which helps to better understand the industry dynamics.The data used in this study is Volatility Index of S&P 500, and major wind turbine manufacturers namely Vestas Wind Systems A/S closing value in the Copenhagen stock exchange, Xinjiang GoldWind Science & Technology Co., Ltd. closing value in the Hong Kong stock exchange, and GAMESA closing value in the Frankfurt stock exchange. The research period is between 8 October 2010 and 6 July 2015. The data is daily and the total number of data is 1193. The long term relationship between the variables is analyzed with Johansen’s Cointegration methodology. This indicates that there is a long-run relationship between the stock exchange volatility and wind turbine manufacturing firms’ performance. For the short run analysis a VECM is developed. Granger causality analysis is also provided. This suggests there is no causality in neither ways between VIX , and firms’ stock market value namely VESTAS, GOLDWIND, and GAMESA. This shows that stock market volatility fail to represent the changes in wind turbine manufacturing firms’ performance in the short run although there is a relationship in the long run. This indicates that wind turbine manufacturing industry shows its own dynamics in the short run as explained in this paper. Knowledge on specific opportunities and threats for the industry especially in the field of finance is required for successful performance of the companies.
Suggested Citation
Cem Berk, 2016.
"Opportunities and Risks in Wind Energy Finance: Testing Volatility For Turbine Manufacturers,"
Eurasian Business & Economics Journal, Eurasian Academy Of Sciences, vol. 1(01), pages 470-478, February.
Handle:
RePEc:eas:buseco:v:01:y:2016:i:01:p:470-478
DOI: 10.17740/eas.econ.2016-MSEMP-41
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