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

Analyzing interconnection among selected commodities in the 2008 global financial crisis and the COVID-19 pandemic

This study investigates the interconnection among several commodities in the advent of two well-known phenomena: the 2008 global financial crisis (GFC) and the COVID-19 pandemic. We use a daily return series for selected commodities: three base metals (copper, zinc, and lead), two benchmark crude oils (WTI and Brent), and gold. Three different methods have been considered to study interconnection: Multifractality, Network theory, and Wavelet coherences. By applying Detrending Moving-average Cross-correlation Analysis (DMCA) method, we witnessed an increase in cross-correlation in the higher time windows in most time series. Generally, we observe that the benchmark crude oils have the highest relationships, and then, in the following positions, we have the dependency among base metals (copper, lead, and zinc) and between the base metals and the crude oils. In the context of the Wavelet analysis, we notice that the significant fluctuations and changes in the extent of interconnections among data could be traced when the two crises occurred, particularly between October 2018 and April 2021, and in the frequency range of 4-128 days. This phenomenon indicates the role of the COVID-19 pandemic in creating a volatile situation in the commodity markets. The findings of this study have significant implications for investors, academic researchers, and policymakers.

Analyzing interconnection among selected commodities in the 2008 global financial crisis and the COVID-19 pandemic

This study investigates the interconnection among several commodities in the advent of two well-known phenomena: the 2008 global financial crisis (GFC) and the COVID-19 pandemic. We use a daily return series for selected commodities: three base metals (copper, zinc, and lead), two benchmark crude oils (WTI and Brent), and gold. Three different methods have been considered to study interconnection: Multifractality, Network theory, and Wavelet coherences. By applying Detrending Moving-average Cross-correlation Analysis (DMCA) method, we witnessed an increase in cross-correlation in the higher time windows in most time series. Generally, we observe that the benchmark crude oils have the highest relationships, and then, in the following positions, we have the dependency among base metals (copper, lead, and zinc) and between the base metals and the crude oils. In the context of the Wavelet analysis, we notice that the significant fluctuations and changes in the extent of interconnections among data could be traced when the two crises occurred, particularly between October 2018 and April 2021, and in the frequency range of 4–128 days. This phenomenon indicates the role of the COVID-19 pandemic in creating a volatile situation in the commodity markets. The findings of this study have significant implications for investors, academic researchers, and policymakers.

Annotating Protein Structures for Understanding SARS-CoV-2 Interactome

Protein Contact Network (PCN) is an emerging paradigm for modelling protein structure. A common approach to interpreting such data is through network-based analyses. It has been shown that clustering analysis may discover allostery in PCN. Nevertheless Network Embedding has shown good performances in discovering hidden communities and structures in network. SARS-CoV-2 proteins, and in particular S protein, have a modular structure that need to be annotated to understand complex mechanism of infections. Such annotations, and in particular the highlighting of regions participating in the binding of human ACE2 and TMPRSS, may help the design of tailored strategy for preventing and blocking infection. In this work, we compare some approaches for graph embedding with respect to some classical clustering approaches for annotating protein structures. Results shows that embedding may reveal interesting structure that constitute the starting point for further analysis. © 2022 CEUR-WS. All rights reserved.

A STUDY OF COMPUTATIONAL AND CONCEPTUAL COMPLEXITIES OF COMPARTMENT AND AGENT BASED MODELS

The ongoing COVID-19 pandemic highlights the essential role of mathematical models in understanding the spread of the virus along with a quantifiable and science-based prediction of the impact of various mitigation measures. Numerous types of models have been employed with various levels of success. This leads to the question of what kind of a mathematical model is most appropriate for a given situation. We consider two widely used types of models: equation-based models (such as standard compartmental epidemiological models) and agent-based models. We assess their performance by modeling the spread of COVID-19 on the Hawaiian island of Oahu under different scenarios. We show that when it comes to information crucial to decision making, both models produce very similar results. At the same time, the two types of models exhibit very different characteristics when considering their computational and conceptual complexity. Consequently, we conclude that choosing the model should be mostly guided by available computational and human resources.