Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model

Wei, Yu; Yu, Qianwen; Liu, Jing; Cao, Yang

doi:10.1016/j.physa.2017.11.022

Cited by 53 publications

(35 citation statements)

References 22 publications

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“…Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system. In the future, the application of the proposed Dice similarity measure of PFSs needs to be explored in decision making, [73][74][75][76][77][78][79][80][81][82][83][84] risk analysis 85,86 and many other fields under uncertain environment. [87][88][89][90][91][92][93][94]…”

Section: Discussionmentioning

confidence: 99%

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

2019

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The Pythagorean fuzzy set (PFS) is characterized by two functions expressing the degree of membership and the degree of nonmembership, which square sum of them is equal or less than 1. It was proposed as a generalization of a fuzzy set to deal with indeterminate and inconsistent information. In this study, we shall present some novel Dice similarity measures of PFSs and the generalized Dice similarity measures of PFSs and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures‐based multiple attribute group decision‐making models with Pythagorean fuzzy information. Then, we apply the generalized Dice similarity measures between PFSs to multiple attribute group decision making. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system.

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Section: Discussionmentioning

confidence: 99%

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

2019

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“…Finally, a numerical example for performance assessment of new rural construction has been given to demonstrate the application of bidirectional project method of dual hesitant Pythagorean fuzzy, and we used the dual hesitant Pythagorean weighted Bonferroni mean to compare its reasonable and effectiveness. In the future work, we shall use more MADM methods in DHPFS and develop the applications in other situation …”

Section: Resultsmentioning

confidence: 99%

Bidirectional project method for dual hesitant Pythagorean fuzzy multiple attribute decision‐making and their application to performance assessment of new rural construction

Tang

Wei

et al. 2019

Int J Intell Syst

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The dual hesitant Pythagorean fuzzy set (DHPFS) consists of two parts, that is, the membership hesitancy function and the nonmembership hesitancy function, supporting a more exemplary and flexible access to assign values for each element in the domain. It is very suitable to handle the situation that there are various possible values in membership and nonmembership degrees to depict the true circumstance. The bidirectional project method of DHPFS calculates method considered not only the bidirectional projection magnitudes and the distance but also includes angle between objects evaluated. Therefore, this paper proposes a bidirectional project method of DHPFS to handle the multiple attribute decision-making (MADM) problem under the dual hesitant Pythagorean fuzzy environment. Through the measure between each alternative decision matrix and the positive and negative ideal alternative matrix, the ranking order all alternatives can be used to select the best alternative. Furthermore, a model for MADM has been given. Finally, a numerical example for performance assessment of new rural construction has been given to demonstrate the application of bidirectional project method of DHPFS, and we used the dual hesitant Pythagorean weighted Bonferroni mean to compare its reasonable and effectiveness. K E Y W O R D Sbidirectional project method, dual hesitant Pythagorean fuzzy set, multiple attribute decision-making, new rural construction, performance assessment 1 | INTRODUCTION Atanassov 1,2 introduced the concept of intuitionistic fuzzy set (IFS) characterized by a membership function and a nonmembership function, which is a generalization of the concept of fuzzy set, 3 whose basic component is only a membership function. For the IFS, the sum of the membership and nonmembership degrees of each ordered pair is less than or equal to 1. Xu 4 developed the intuitionistic fuzzy weighted averaging (IFWA) operator, the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator, and the intuitionistic fuzzy hybrid aggregation (IFHA) operator. Xu 5 developed some geometric aggregation operators, such as the intuitionistic fuzzy weighted geometric (IFWG) operator, the intuitionistic fuzzy ordered weighted geometric (IFOWG) operator, and the intuitionistic fuzzy hybrid geometric (IFHG) operator and gave an application of the IFHG operator to multiple attribute group decision-making with intuitionistic fuzzy information. Zhao and Wei 6 proposed some intuitionistic fuzzy Einstein hybrid aggregation operators for multiple attribute decision-making (MADM). Li et al 7 gave some methods for multiple attribute group decision-making based on intuitionistic fuzzy Dombi Hamy mean operators. The IFS and their extensions have received more and more attention since its appearance. A new extension of IFS, namely, the Pythagorean fuzzy set (PFS), 31,32 has been developed. The PFS is also characterized by the membership and nonmembership degrees. The difference between PFS and IFS is that the square sum of the membership and nonmembership de...

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“…The q-ROFWHM and q-ROFWDHM operators were distinguished from other existed operators not only due to the fact that the operators accommodate the q-ROFNs, but also due to the consideration of the interdependent phenomena among the arguments, which allows our operators to have more wide practical application potentials. In the future, the application of the proposed aggregating operators of q-ROFSs needs to be explored in the decisionmaking, 63-67 risk analysis, [68][69][70] and many other fields under uncertain environment. [71][72][73][74][75][76][77][78]…”

Section: Discussionmentioning

confidence: 99%

Some q ‐rung orthopair fuzzy Hamy mean operators in multiple attribute decision‐making and their application to enterprise resource planning systems selection

Wang

Wei

et al. 2019

Int J Intell Syst

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In this paper, the Hamy mean (HM) operator, weighted HM (WHM), dual HM (DHM) operator, and dual WHM (WDHM) operator under the q‐rung orthopair fuzzy sets (q‐ROFSs) is studied to propose the q‐rung orthopair fuzzy HM (q‐ROFHM) operator, q‐rung orthopair fuzzy WHM (q‐ROFWHM) operator, q‐rung orthopair fuzzy DHM (q‐ROFDHM) operator, and q‐rung orthopair fuzzy weighted DHM (q‐ROFWDHM) operator and some of their desirable properties are investigated in detail. Then, we apply these operators to multiple attribute decision‐making problems. Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model

Cited by 53 publications

References 22 publications

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

Bidirectional project method for dual hesitant Pythagorean fuzzy multiple attribute decision‐making and their application to performance assessment of new rural construction

Some q ‐rung orthopair fuzzy Hamy mean operators in multiple attribute decision‐making and their application to enterprise resource planning systems selection

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