Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

A Decision Making Approach for Street Lockdown to Cope with Covid-19 Cases by Using Shortest Path Selection Mechanism for Unplanned Colonies

Infectious disease syndrome like covid-19 falls under the Public health domain and needs to be addressed with timely decisions and rapid actions. For such diseases, the dispersal becomes exponential with frequent social gatherings, therefore the immediate strategy, to control the surging waves of covid-19, was to impose immediate lockdown of COVID-19 infected zones. In this paper, the concept of street networks has been incorporated with shortest path algorithm e.g. minimum spanning tree (MST) to define an approach to investigate the correlation between reported COVID-19 cases and relevant streets in order to adopt better lockdown strategy for unplanned colonies. Geo-spatial representation has been used for subsequent composition of patterns to identify the particular streets for locked down. Results show that MST provides better solution by evaluating explicit areas of concern for lockdown plans. © 2022 IEEE.

The generalized STAR modelling with minimum spanning tree approach of weight matrix for COVID-19 case in Java Island

The ongoing global Coronavirus 2019 (COVID-19) pandemic poses a major threat. The spread of the COVID-19 virus is likely to occur from one location to another location due to the mobility of people. Many efforts and policies have been made by each country to reduce the spread of the COVID-19 outbreak. The imposition of lockdown and large-scale social restrictions or social distancing has been widely applied to limit the transmission of this virus among the community and provincial levels. Both policies have proven effective in reducing the spread of the COVID-19 virus. To obtain the overview of this case, many researchers were conducted. Here, the Generalized STAR (GSTAR) model was applied to model the increasing number of COVID-19 positive cases per day in six provinces in Java Island. The data was recorded simultaneously in six locations, namely in the Provinces of Banten, Jakarta, West Java, Central Java, Yogyakarta Special Region, and East Java. This paper proposes a new approach in constructing the weight matrix required to build the GSTAR model, namely Minimum Spanning Tree (MST). The weight matrix represents the relationship among observed locations. By using the MST, a topological (undirected graph) network model could be created to show the correlation, centrality, and relationship on the increase of COVID-19 positive cases among the provinces in Java Island. The GSTAR(1;1) with the inverse distance weight matrix using MST presents a good ability to predict the COVID-19 increasing cases of Java island. This is indicated by the final MAPE average score of 19.55. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.