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Stock markets exhibit different states due to internal and external shocks. It is of great significance to identify the status of global financial markets. Based on 48 global stock indices from 1996 to 2018, we constructed a global stock index transfer entropy network, which represents the information flow between stock markets in different economies. By analyzing the triadic motifs in the transfer entropy network, we divided the global stock market into different market states based on the distribution of motifs. The characteristic structure of information flow was observed in different market states. We found that the information flow in the global stock market increases significantly during major financial events, indicating that the global stock index has a mutual influence and a close relationship with each other. In addition, stock indices in the Asia-Pacific, Middle East, and Africa are the main sinks of information, while stock indices in the Americas and Europe are the primary sources of information.  相似文献   
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ABSTRACT

The relationship between green hotel service attributes and consumption experiences remains unclear in the extant research, especially in the context of emerging economies such as India. This work uses a multi-method approach that combines in-depth interviews, word association and two-stage empirical validation to propose a three-dimensional framework for measuring a hotel’s green servicescape, composed of atmospherics, motifs and human encounters. Individual effects of each green servicescape sub-dimension on those of green experiential values, namely utilitarian, emotional, social and altruistic values, are examined. The results reveal interesting findings, some counterintuitive, which are expected to create new insights for academicians and practitioners alike.  相似文献   
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In this article we propose to exploit topological information embedded in forecast error variance decomposition derived from large Bayesian vector autoregressive models (VAR) to study network connectedness and risk transmission of multivariate time series observations. Firstly, we design a robust link classification procedure based on shortest paths, so to identify salient directional spillovers in a high-dimensional framework. Secondly, we study recurrent and statistically significant sub-graphs, i.e. network motifs, on the induced network backbone by means of null models which account for local node heterogeneity. The methodology is applied to analyze spillover networks of a set of global commodity prices. We demonstrate that spillovers become key drivers of the system variance during commodity price bubbles and bursts, giving raise to complex triadic structures which do not manifest during normal business periods. By accounting for local node connectivity, we observe a departure from the null models due to the high participation of Crude Oil, Food and Beverages and Raw Materials in complex recurrent sub-graphs.  相似文献   
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