基于Logistic生长方程——马尔可夫模型的港口投资风险测度
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摘要
伴随着我国经济从高速增长逐渐向经济新常态转变,港口的发展也从高速增长阶段逐渐趋近平稳,随之而来的港口风险问题日益突出。由于港口的发展同中国经济发展相似,呈现非常明显的非线性发展特征,这样采用传统的单一模型很难刻画这一现实问题。为此,本文首次构造了Logistic生长方程—马尔可夫组合模型,通过Logistic生长方程生成马尔可夫模型所需要的状态参数,在此基础上构造风险转移概率矩阵,有效的解决了港口投资风险的测度问题。本文选取1985至2014年的样本数据进行验证,结果表明:2016年后港口投资风险会突显出来,并将持续较长一段时期,表明我国港口正处在转型升级的关键时期,需加快从外延式扩张向内涵式发展转变,从粗放式到精细化管理转变。
China's economy is shifting from high speed growth to"new normal"development,the throughput capacity of the port also turn to slow down,the port risk problem is becoming more and more obvious.The development of the port is similar to China's economic development,and showing a very obvious nonlinear development features,it is difficult to describe the realistic problem by using the traditional single model.In this paper,the logistic growth model is initially combined with the Markov model,then logistic growth equation generates the state parameters that Markov model needs,and based on this fact,a risk transfer probability matrix is constructed.So the problem about the port investment risk measure can be effectively solved.The sample data from 1985 to 2014 is selected to verify the results,and the results show that the port investment risk will be highlighted after 2016,and will last for a longer period of time.China's port is currently in a critical period of transformation and upgrading,and it is necessary to accelerate the transition from extensive expansion to the connotation of the development,from extensive to detailed management.
China's economy is shifting from high speed growth to"new normal"development,the throughput capacity of the port also turn to slow down,the port risk problem is becoming more and more obvious.The development of the port is similar to China's economic development,and showing a very obvious nonlinear development features,it is difficult to describe the realistic problem by using the traditional single model.In this paper,the logistic growth model is initially combined with the Markov model,then logistic growth equation generates the state parameters that Markov model needs,and based on this fact,a risk transfer probability matrix is constructed.So the problem about the port investment risk measure can be effectively solved.The sample data from 1985 to 2014 is selected to verify the results,and the results show that the port investment risk will be highlighted after 2016,and will last for a longer period of time.China's port is currently in a critical period of transformation and upgrading,and it is necessary to accelerate the transition from extensive expansion to the connotation of the development,from extensive to detailed management.
引文
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[23]张晨,杨玉,张涛.基于Copula模型的商业银行碳金融市场风险整合度量[J].中国管理科学,2015,23(4):61-69.
[24]吴建华,王新军.内生性回收率与信用风险违约度量研究[J].中国管理科学,2016,24(1):1-10.
[25]Ramchand L,Susmel R.Variance and covariances of international stock returns:The international capital asset pricing model revisited[J].Journal of International Financial Markets,1998,8(1):39-57.
[26]罗平,姜仁荣,李红旮,等.基于空间Logistic和Markov模型集成的区域土地利用演化方法研究[J].中国土地科学,2010,24(1):31-36.
[27]张桦,魏本刚,李可军,等.基于变压器马尔可夫状态评估模型和熵权模糊评价法的风险评估技术研究[J].电力系统保护与控制,2016,44(5):134-140.
[2]van Ham H,KoppenjanJ.Building public-private partnerships:Assessing and ma-naging risks in port development[J].PublicManagement Review,2001,3(4):593-616.
[3]Lam J S L,Su S.Disruption risks and mitigation strategies:An analysis of Asian ports[J].Maritime Policy&Management,2015,42(5):415-435.
[4]Asteris M,Collins A.UK container port investment and competition:Impediments to the market[J].Transport Reviews,2010,30(2):163-178.
[5]Koh Y K.Optimal investment priority in container port development[J].Maritime Policy&Management,2001,28(2):109-123.
[6]Rodrigue J P,Notteboom T,Pallis A.The financialization of the port and terminal industry:Revisitingrisk and embeddeness[J].Maritime Policy&Management,2011,38(2):191-213.
[7]Simkins Z S,Stewart R D.Factoring a real value of cargo in port investment a-nd funding decisions[J].Maritime Policy&Management,2015,42(7):712-728.
[8]Chen M.The use of the interest rate spread for forecasting business cycle turning points and asset allocation:A Markov switch approach[R].Working Paper,2000.
[9]王春峰,万海辉,李刚.基于MCMC的金融市场风险VaR的估计[J].管理科学学报,2000,3(2):54-61.
[10]Siu T K,Ching W K,Fung S E,et al.On a multivariate Markov chain model for credit risk measurement[J].Quantitative Finance,2005,5(6):543-556.
[11]高金余,陈翔.马尔可夫切换模型及其在中国股市中的应用[J].中国管理科学,2007,15(6):20-25.
[12]杨继平,张春会.基于马尔可夫状态转换模型的沪深股市波动率的估计[J].中国管理科学,2013,21(2):42-48.
[13]陈守东,王妍.我国金融机构的系统性金融风险评估—基于极端分位数回归技术的风险度量[J].中国管理科学,2014,22(7):10-17.
[14]Liang Xue,Dong Yinghui.A Markov chain copula model for credit default swa-ps with bilateral counterparty risk[J].Communications in Statistics Theory and Meth-ods,2014,43(3):498-514.
[15]黄杰.沿海港口吞吐量与国民经济关系研究[D].大连:大连海事大学博士论文,2011.
[16]钟铭,吴艳云,栾维新.港口物流与城市经济协同度模型[J].大连海事大学学报,2011,37(1):80-82.
[17]王洪青,祁春节,刘欢.港口对腹地经济贡献弹性的U型曲线及其理论解释[J].地域研究与开发,2013,32(2):22-26.
[18]李电生,员丽芬.港口群物流系统效率评价[J].北京交通大学学报,2010,9(2):42-47.
[19]张小蒂,邓娟.中国港口效率测度及提升研究[J].浙江大学学报(人文社会科学版),2013,43(4):39-51.
[20]汪传旭,崔建新.长江三角洲港口群物流系统动力学分析模型[J].交通运输工程学报,2007,7(5):77-83.
[21]丁国蕾,刘云啸,王晓光.长江三角洲主要港口间的协调发展机制[J].城市发展研究,2016,23(3):64-71.
[22]隋聪,谭照林,王宗尧.基于网络视角的银行业系统性风险度量方法[J].中国管理科学,2016,24(5):54-64.
[23]张晨,杨玉,张涛.基于Copula模型的商业银行碳金融市场风险整合度量[J].中国管理科学,2015,23(4):61-69.
[24]吴建华,王新军.内生性回收率与信用风险违约度量研究[J].中国管理科学,2016,24(1):1-10.
[25]Ramchand L,Susmel R.Variance and covariances of international stock returns:The international capital asset pricing model revisited[J].Journal of International Financial Markets,1998,8(1):39-57.
[26]罗平,姜仁荣,李红旮,等.基于空间Logistic和Markov模型集成的区域土地利用演化方法研究[J].中国土地科学,2010,24(1):31-36.
[27]张桦,魏本刚,李可军,等.基于变压器马尔可夫状态评估模型和熵权模糊评价法的风险评估技术研究[J].电力系统保护与控制,2016,44(5):134-140.