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基于Monte-Carlo的城轨PPP项目投资风险评估 被引量:3

Investment Risk Assessment of Urban Rail PPP Project Based on Monte-Carlo
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摘要 近年来,城市轨道交通PPP项目飞速发展,但由于政府与社会资本信息不对称,两者矛盾隐患日益突出,甚至影响了项目的推进,而合理的投资回报机制能够大幅减少两者的博弈及谈判消耗。文章先对其风险因素进行了定性分析,然后运用蒙特卡罗模拟与投资风险计量指标,构建了包含基于VaR+CVaR的净现值分析、基于标准离差率的内部收益率分析、多因素敏感性分析三部分的投资风险评估模型。最后结合具体项目论证了该行业具有较好的投资价值,同时相比协议票价法,与绩效考核相结合的车公里服务费法跟当前发展阶段更加匹配。研究有望为咨询公司设计回报机制及项目公司降低投资风险提供理论指导及实践依据。 In recent years,the PPP project of urban rail transit has developed rapidly.However,due to the asymmetric information between the government and social capital,the contradiction between the two has become increasingly prominent,even affecting the progress of the project.A reasonable mechanism of return on investment can significantly reduce the consumption of game and negotiation between the two projects.This paper first makes a qualitative analysis of its risk factors,and then uses Monte Carlo simulation and investment risk measurement indicators to construct an investment risk assessment model which includes three parts:net present value analysis based on VaR and CVaR,internal rate of return analysis based on standard deviation rate and multi-factor sensitivity analysis.At last,it demonstrates that the industry has a better investment value with specific projects.At the same time,compared with the agreement fare method,the vehicle kilometer service fee method combined with performance appraisal is more compatible with the current development stage.The research is expected to provide theoretical guidance and practical basis for consulting companies to design return mechanism and project companies to reduce investment risk.
作者 韩红霞 刘松 崔武文 HAN Hong-xia;LIU Song;CUI Wu-wen(School of Civil and Transportation Engineering,Hebei University of Technology,Tianjin 300401,China;School of Management Science and Engineering,Tianjin University of Finance and Economics,Tianjin 300204,China)
出处 《土木工程与管理学报》 北大核心 2020年第1期75-81,共7页 Journal of Civil Engineering and Management
关键词 城市轨道交通 PPP 风险因素 蒙特卡罗 风险评估 敏感性分析 urban rail transit PPP risk factors Monte Carlo risk assessment sensitive factors analysis
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