To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation...To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation weights.The comprehensive attribute weights are gotten through the product of the above three kinds of weights.And each decision maker＇s weighted decision matrices are also received by using the integrated attribute weights.The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution（TOPSIS） through dealing with the weighted decision matrices.At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker＇s closeness degree to every project.Then the vertical TOPSIS method is used to calculate the closeness degree of each project.So these projects can be ranked according to their values of the closeness degree.The process of the method is also given step by step.Finally,a numerical example demonstrates the feasibility and effectiveness of the approach.展开更多
Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regio...Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regional and industrial risk contagion embedded within them. By providing a theoretical framework of a loan guarantee network, a method is proposed for calculating the amount of risk spillover caused by loan guarantees taking the perspective of the entire network. In addition,the route of risk contagion in guarantee networks is analyzed, revealing that when default risk shocks occur, risk contagion travels along the nodes not once but for several rounds and that the risk control of one firm cannot prevent these systemic risks. Therefore, a risk control scheme is designed based on the location and importance of firms in the network. Using data from a real guarantee network,we demonstrate that identifying the node locations of firms' in the guarantee network(including the coritivity and closeness of the firm) can help in understanding risk contagion mechanisms and preventing systemic credit risk before a crisis occurs.展开更多
基金supported by the Research Innovation Project of Shanghai Education Committee (08YS19)the Excellent Young Teacher Project of Shanghai University
文摘To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation weights.The comprehensive attribute weights are gotten through the product of the above three kinds of weights.And each decision maker＇s weighted decision matrices are also received by using the integrated attribute weights.The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution（TOPSIS） through dealing with the weighted decision matrices.At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker＇s closeness degree to every project.Then the vertical TOPSIS method is used to calculate the closeness degree of each project.So these projects can be ranked according to their values of the closeness degree.The process of the method is also given step by step.Finally,a numerical example demonstrates the feasibility and effectiveness of the approach.
基金supported by the National Nature Science Foundation of China under Grant Nos.71172186,71472148,71572144 and 71502138
文摘Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regional and industrial risk contagion embedded within them. By providing a theoretical framework of a loan guarantee network, a method is proposed for calculating the amount of risk spillover caused by loan guarantees taking the perspective of the entire network. In addition,the route of risk contagion in guarantee networks is analyzed, revealing that when default risk shocks occur, risk contagion travels along the nodes not once but for several rounds and that the risk control of one firm cannot prevent these systemic risks. Therefore, a risk control scheme is designed based on the location and importance of firms in the network. Using data from a real guarantee network,we demonstrate that identifying the node locations of firms' in the guarantee network(including the coritivity and closeness of the firm) can help in understanding risk contagion mechanisms and preventing systemic credit risk before a crisis occurs.
