Tongtao Ma
School of Economics and Management, North China Electric Power University, Beijing, 102206, China
Cunbin Li
School of Economics and Management, North China Electric Power University, Beijing, 102206, China
ABSTRACT
The Chinese government is promoting energy conservation, environmental protection policies. Development of new energy generation projects has a very important significance. New energy construction projects,which has a long cycle and more technical problems, have high investment risks. Based on the characteristics of new energy projects, the model sets constraints. In order to maximize the overall revenue and reduce investment risk, this study uses CVaR theory to establish investment optimization model. And an example was given to verify the validity of the model based on the actual data. The results show that CVaR investment portfolio optimization model is able to optimize the ratio of investment and reduce investment risk of loss. There are certain practical value.
PDF References Citation
How to cite this article
Tongtao Ma and Cunbin Li, 2013. New Energy Project Investment Risk Computability- Optimization Model. Information Technology Journal, 12: 3299-3302.
DOI: 10.3923/itj.2013.3299.3302
URL: https://scialert.net/abstract/?doi=itj.2013.3299.3302
DOI: 10.3923/itj.2013.3299.3302
URL: https://scialert.net/abstract/?doi=itj.2013.3299.3302
REFERENCES
- Sarica, K. and I. Or, 2007. Efficiency assessment of turkish power plants using date envelopmentanalysis. Energy, 32: 1484-1489.
CrossRefDirect Link - Vaninsky, A., 2006. Efficiency of electric power generation in the United States: Analysis and forecast based on date envelopment analysis. J. Energy Econ., 28: 326-338.
CrossRefDirect Link - Chen, F.Y., 2011. Analytical VaR for international portfolios with common jumps. Comput. Math. Appli., 62: 3066-3076.
CrossRefDirect Link - Claro, J. and J.P. de Sousa, 2012. A multiobjectivemetaheuristic for a mean-risk multistage capacity investment problem with process flexibility. Comput. Operat. Res., 39: 838-849.
CrossRefDirect Link - Goh, J.W., K.G. Lim, M. Sim and W. Zhang, 2012. Portfolio value-at-risk optimization for asymmetrically distributed asset returns. Eur. J. Operat. Res., 221: 397-406.
Direct Link - Schaumburg, J., 2012. Predicting extreme value at risk: Nonparametric quantile regression with refinements from extreme value theory. Comput. Stat. Data Anal., 56: 4081-4086.
CrossRefDirect Link - Glasserman, P., P. Heidelberger and P. Shahabuddin, 2002. Portfolio value-at-risk with heavy-tailed risk factors. Math. Fin., 12: 239-269.
Direct Link - Lim, A.E.B., J.G. Shanthikumar and G.Y. Vahn, 2011. Conditional value-at-risk in portfolio optimization: Coherent but fragile. Operat. Res. Lett., 39: 163-171.
CrossRefDirect Link