Research on price transmission in Chinese mining stock market: Based on industry

The strong impact of COVID-19 on the global mining market has caused severe fluctuations in the prices of mineral products and mining stocks. Meanwhile, geopolitical conflicts have exacerbated risks in minerals trade and mining stock transactions. In the face of uncertainties in the international economic landscape and volatility of stock prices, China, as the world's major mineral trading country, has become increasingly linked between its stock market and the mining economy. To clarify the characteristics of mining stock price fluctuations and the evolution of the transmission relationships, and identify the key nodes and main paths of price transmission, we select 100 Chinese mining stocks from January 2019 to October 2022, distinguish them according to the industry category, and use Granger causality test, minimum spanning tree model and complex network analysis method to study. The results show that: (1) Chinese mining stock prices have risen significantly since 2020, and there has been a "decoupling” phenomenon within the stock market, that is, the linkage between some mining stocks has weakened. (2) The stock price fluctuation characteristics and transmission effects of different mining industries are obviously different. Precious metal minerals (PM) have the most dramatic changes in price fluctuations, the most prominent hedging characteristics, and the rapid price response ability, which is the first to accept price transmission. rare earth and rare metal minerals (RE) are sensitive to price fluctuations and are usually the "leader” of the transmission path. Bulk non-ferrous minerals (BNFM) have the most stable price fluctuations and are closely related to other stocks, which is a "transit warehouse” in the transmission path. (3) The price transmission mechanism of Chinese mining stock market has gradually stabilized, and the main transmission paths of "Coal→Agricultural minerals (Agri)→BNFM→Steel” and "PM, Core minerals for new energy (NEM), and RE→BNFM” have been formed in 2022.

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

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Purpose: COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network. Design/methodology/approach: Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs. Findings: The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node;however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed. Originality/value: It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere. © 2022, Emerald Publishing Limited.

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.

The impact of COVID-19 on the topological properties of the Moroccan stock market network

This paper investigates the topological evolution of the Casablanca Stock Exchange (CSE) from the perspective of the Coronavirus 2019 (COVID-19) pandemic. Crosscorrelations between the daily closing prices of the Moroccan most active shares (MADEX) index stocks from March 1, 2016 to February 18, 2022 were used to compute the minimum spanning tree (MST) maps. In addition to the whole sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the first year of the COVID-19 pandemic in Morocco. The findings show that, compared to other periods, the mean correlation coefficient increased remarkably through the crisis period;inversely, the mean distance decreased in the same period. The MST and its related tree length support the evidence of the star-like structure, the shrinkage of the MST in times of market turbulence, and an expansion in the recovery period. Besides, the CSE network was less clustered and homogeneous before and after the crisis than in the crisis period, where the banking sector held a key role. The degree and betweenness centrality analysis showed that Itissalat Al-Maghrib and Auto Hall were the most prominent stocks before the crisis. On the other hand, Attijariwafa Bank, Banque Populaire, and Cosumar were the leading stocks during and after the crisis. Indeed, the results of this study can be used to assist policymakers and investors in incorporating subjective judgment into the portfolio optimization problem during extreme events. © Fadwa Bouhlal, Moulay Brahim Sedra, 2022.

Analysis of the risk-transmitting nature of European stock indices.

This study analyses the structural changes in European stock market indices using a minimal spanning tree and a Markov-switching model that examines the regime-changes in betweenness and closeness in addition to topological changes. The authors aim is to highlight the similarities and differences of previous recessions, namely the subprime mortgage crisis of 2008, the European sovereign debt crisis of 2010, and the recent period of Covid-19. Focusing on the structural changes in the graph, the appearance of a central index transmitting shocks is sought. The results show that there is a constant change in the stock market network, where stock market indices are linked to each other mainly through a central index in turbulent periods, while relationships become more diversified in calm periods. (English) [ FROM AUTHOR] A tanulmány az európai részvénypiacok szerkezeti változásait elemzi minimális feszítőfa és Markov-féle rezsimváltó modell segítségével, amely a topológiai változások mellett a közelség és a közöttiség segítségével vizsgálja az adatokat. A szerzők célja, hogy rámutassanak a korábbi receszsziók hasonlóságaira és különbségeire, nevezetesen a 2008-as másodlagos jelzálogpiaci válságra, a 2010-es évek európai államadósság-válságára és a közelmúltban a Covid19-járvány időszakára. A szerkezeti változásokra fókuszálva egy sokkokat továbbító központi index megjelenését keresik. Folyamatos változást tapasztalnak a tőzsdei hálózatban, ahol a tőzsdeindexek a turbulens időszakokban főként egy központi indexen keresztül kapcsolódnak egymáshoz, míg a nyugodt időszakokban a kapcsolatok diverzifikáltabbá válnak. (Hungarian) [ FROM AUTHOR] Copyright of Hungarian Statistical Review / Statisztikai Szemle is the property of Hungarian Central Statistical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Financial Market in China under the COVID-19

The main aim of this study is to investigate the effects of COVID-19 on financial markets in China. Results of correlation analysis indicate that higher financial correlation among provinces emerged after the official announcement regarding COVID-19 in China. The Minimum Spanning Tree (MST) results after the pandemic announcement denote that Shanghai, Beijing, Jiangsu, Zhejiang, and Chongqing become the new cores, and the overall linking type exhibits cluster mode, which is varied from the intertwined connection mode. In addition, through Ensemble Empirical Mode Decomposition (EEMD) and Wavelet analysis, we found that financial markets in China are more susceptible to unexpected incidents.

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Purpose COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network. Design/methodology/approach Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs. Findings The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node;however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed. Originality/value It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.

The structure of the South African stock market network during COVID-19 hard lockdown

This study investigates the topology of the South African stock market network pre, during, and post the level 5-lockdown period. We use the daily closing price of the 134 companies in the all-share index (ALSI) from 01 October 2019 to 30 October 2020. We construct the minimum spanning tree using the cross-correlation of the returns computed from the closing price data. The research findings show that the South African stock market network forms clusters and is homogenous, and the finance industry plays a central role. Specifically, the results show an expansion of MST during the level 5 lockdown and shrinkage of the MST post level 5 lockdown. The average correlation coefficient decreases through all sub-periods;conversely, the average distance increases in all sub-periods. Post-level 5 lockdown period, stocks in the Health Care Equipment & Services sector form a small cluster that did not exist before the lockdown period. JSE node degree distribution for all sub-periods follows the power law.

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.

SARS-CoV-2 surveillance in Italy through phylogenomic inferences based on Hamming distances derived from pan-SNPs, -MNPs and -InDels.

BACKGROUND: Faced with the ongoing global pandemic of coronavirus disease, the 'National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis' (GENPAT) formally established at the 'Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise' (IZSAM) in Teramo (Italy) is in charge of the SARS-CoV-2 surveillance at the genomic scale. In a context of SARS-CoV-2 surveillance requiring correct and fast assessment of epidemiological clusters from substantial amount of samples, the present study proposes an analytical workflow for identifying accurately the PANGO lineages of SARS-CoV-2 samples and building of discriminant minimum spanning trees (MST) bypassing the usual time consuming phylogenomic inferences based on multiple sequence alignment (MSA) and substitution model. RESULTS: GENPAT constituted two collections of SARS-CoV-2 samples. The first collection consisted of SARS-CoV-2 positive swabs collected by IZSAM from the Abruzzo region (Italy), then sequenced by next generation sequencing (NGS) and analyzed in GENPAT (n = 1592), while the second collection included samples from several Italian provinces and retrieved from the reference Global Initiative on Sharing All Influenza Data (GISAID) (n = 17,201). The main results of the present work showed that (i) GENPAT and GISAID detected the same PANGO lineages, (ii) the PANGO lineages B.1.177 (i.e. historical in Italy) and B.1.1.7 (i.e. 'UK variant') are major concerns today in several Italian provinces, and the new MST-based method (iii) clusters most of the PANGO lineages together, (iv) with a higher dicriminatory power than PANGO lineages, (v) and faster that the usual phylogenomic methods based on MSA and substitution model. CONCLUSIONS: The genome sequencing efforts of Italian provinces, combined with a structured national system of NGS data management, provided support for surveillance SARS-CoV-2 in Italy. We propose to build phylogenomic trees of SARS-CoV-2 variants through an accurate, discriminant and fast MST-based method avoiding the typical time consuming steps related to MSA and substitution model-based phylogenomic inference.

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network.

BACKGROUND: Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. METHODS: By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study. RESULTS: The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.