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A network for world stock market indices constructed from a 20-day window from 21 August 2009 to 20 September 2009. Edges with weights below 0.85 are excluded (unconnected). Yellow nodes represent stock market indices from the Asia Pacific region; red for the Americas; blue for Europe; and black for the Africa/Middle East region. Only 50 countries are shown in this graph. The rest of the countries are disconnected with no edge weight larger than the threshold.
Source publication
This paper studies the cross-correlations of 67 stock market indices in the past 5 years. In order to capture the interaction of the stock markets, we propose to take a complex network approach to analyzing the interdependence of the individual stock markets. Specifically, stock markets are considered as network nodes, and the network links (weight...
Citations
... We also adopt the sliding window method to determine the time-varying property of GE. Following Liu and Wan's [60] judgment regarding time window width, we use a longer time window width in order to capture the long-term trend of the international stock market accurately. A shorter time window width should be used to analyze the short-term dynamic impacts of fnancial crises, economic cycles, and seasonal factors on stock markets. ...
Several public events have drawn renewed attention to the connectedness of the international stock market since the financial crisis of 2008. We investigate systemic and regional connectedness among stock markets around the world at major public events by constructing correlation networks for 46 markets based on the dynamic time-warping method. We find that (i) geographic regionalization is typically observed in the stock market network, in which France is dominant, (ii) Europe has the greatest and the Middle East and Africa the least within-region connectedness, (iii) the correlation network structure is highly integrated and compact at major public events, and global events influence the international stock market more significantly than regional events do, and (iv) the importance of China reaches its peak during the era of Sino-US trade friction, showing that public events have enormous impacts on the countries involved.
... B Tobias Wand t_wand01@uni-muenster.de 1 developed, emerging and frontier markets [6]. Similar to the contagion effects for stock markets in [5], power grids can also be considered as dynamical complex networks whose topology strongly affects the stability of the system [7]. ...
... Also, one can devise a multi-layered network which devotes one layer to the individual clubs and one layer to the different countries. On the level of different countries, one can define the proximity between two countries by the relative share of transfers between their leagues versus transfers with other countries similar to the correlation between time series in [6] to follow that publication's approach to detecting synchronisation in the transfer network. ...
Using publicly available data from the football database transfermarkt.co.uk, it is possible to construct a trade network between football clubs. This work regards the network of the flow of transfer fees between European top league clubs from eight countries between 1992 and 2020 to analyse the network of each year’s transfer market. With the transfer fees as weights, the market can be represented as a weighted network in addition to the classic binary network approach. This opens up the possibility to study various topological quantities of the network, such as the degree and disparity distributions, the small-world property and different clustering measures. This article shows that these quantities stayed rather constant during the almost three decades of transfer market activity, even despite massive changes in the overall market volume.
... The first strand mainly highlights the degrees of stock market synchronization stratifying different markets based on income groups, integration and union. For instance, Liu and Tse (2012) document that degrees of stock market synchronization are profound in developed markets, whereas emerging markets are less connected. In contrast, Morck et al. (2000) confirm that the degree of synchronization in emerging economies is higher than it is in developed countries (Morck et al. 2000). ...
Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our analysis demonstrates that the total connectedness index (TCI) is 26.15% among the three markets. We find that the US market is the highest volatility contributor, whereas the Russian market is the highest receiver. Since stock market synchronization is exposed to geopolitical risk, at the second stage, we apply the Quantile-on-Quantile framework to measure the response of total and bilateral connectedness indices to geopolitical risk (GPR). The findings affirm our proposition that GPR impedes TCI when it has a bullish state and a higher quantile of GPR. The response of bilateral connectedness is negative towards GPR concerning US–China and US–Russian pairs. However, the degree of connectedness between Russian and Chinese stock markets is less responsive to GPR
... Huang et al. [34] analyzed the return of companies forming Hong Kong's financial market between November 2011 and February 2015 and found a distribution in the power law format for the network formed by the correlation between them. Liu and Tse [35] analyzed the complex network of 32 market indices for several countries finding a strong correlation between volatilities with the exception of developing countries. Matesanz et al. [36] applied complex networks in the commodities market and found a similar dynamic between commodities of the same sector (metals, oils, and grains) but did not observe an increase in the co-movements between them over time with the exception of the years of 2008 and 2009. ...
This paper proposes an analysis of the financial market of 14 countries of the European Union, under the vision of the dynamic networks using the motif-synchronization method. It is found that the countries of Central Europe (France, the Netherlands, Germany, and the UK) are the most influential in the remaining exchanges of the European Union countries. They were also found as hubs during and after the subprime crisis in Ireland and Greece. The network formed between the indices of the countries in the European Union increased its connectivity constantly from 1988 up to 2008 and 2009, years in which the subprime crisis occurred, and after 2008–2009 the connection gradually decreased until the year 2017, revealing behavior before and after the crisis. The results corroborate the thesis that strongly connected financial networks are more susceptible to exogenous shocks than sparse networks.
... Graph theory is good in measuring relations among individuals, such as interpersonal relationships among all football players in a football team. The weight matrix can be calculated by many methods such as Pearson's correlation, Granger causality, ARCH/GARCH-type models, and Copula models (Kenourgios 2014;Kullmann et al. 2002;Liu and Tse 2012;Xiao et al. 2020;Zhang et al. 2010). In addition, two more useful methods need to be introduced. ...
There have been many recent fears of severe house-price decreases in some provinces in China causing a nationwide collapse of the housing market. Therefore, this paper aims to clarify the linkage structure of China’s housing market and its risk contagion routes. Given monthly data of provincial housing and stock-market capital returns from 2001M01 to 2019M12, on the basis of graph theory, this paper first explores the linkage structure of provincial housing markets. Relying on the linkage structure, this paper then simulates the effect of unexpected negative shocks from the stock market on the probabilities of a housing-market collapse based on the epidemic model. The results show that (i) consistently with practical evidence, the probability of housing-market collapse is relatively high in the southwest of China and (ii) reducing housing-market linkage, such as through a blocking mechanism, to prevent collapse is helpful.
... However, such underlying interactions can be well revealed under the networking framework by investigating financial networks' topological properties and statistical characteristics. Liu and Tse [31] used five years of stock index data from 67 countries and use Pearson's correlation to generate a complex network. Gong et al. [32] employed the transfer entropy method to analyse interactions between national stock markets and discovered that countries affected by the crisis become closer to each other and the total network connectedness rises during the crisis. ...
The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the Least Absolute Shrinkage and Selection Operator (LASSO) method to construct the stock network, compare the heterogeneity of bull and bear markets, and further use the Map Equation method to analyse the evolution of modules in the SSE A-shares market. Empirical results show that (1) the connected effect is more significant in bear markets than bull markets and gives rise to abnormal volatilities in the stock market; (2) a system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies.
... Namaki, Shirazi, Raei, & Jafari, 2011;Yang, Li, & Zhang, 2014;Creamer, Ren, & Nickerson, 2013;Nobi, Lee, Kim, & Lee, 2014;Yin, Z. Liu, & P. Liu, 2017;. In a typical stock market network, stocks are taken as nodes of the network, and their edges are described by Pearson correlation coefficient (Liu & Tse, 2012;Kazemilari, Mohamadi, Mardani, & Streimikis, 2019;. We further employ a useful method of Minimum spanning tree (MST), a filtering tool of extracting relevant information from a stock market network (Mantegna, 1999). ...
Purpose – the purpose of this study is to analyse the impact of the recent economic crisis on the network topology structure of Pakistan stock market. Since stock market is considered a core financial market for the development of an economy, it is often used as benchmark to measure a country`s progress. Policymakers often forecast tendency of share prices, that is dependent on several foreign and local macroeconomic factors. Therefore, the aim of this study is to investigate how rising inflation, higher interest rates, and trade and budgetary deficits affect the network structure of blue-chip 96 companies listed on the Karachi stock exchange (KSE-100) index of Pakistan stock market.
Research methodology – this study follows the methodology proposed by Mantegna and Stanley and uses cross-correlation in the daily closing price of KSE 100 Index companies to compute Minimum spanning tree (MST) structures. Additionally, we also apply time-varying topological property of average tree length to extract dynamic features of the MST networks.
Findings – we construct eight monthly MSTs that show the instability of the network structure and significant differences in the topological characteristics due to economic crisis of Pakistan. Furthermore, the time-varying topological property of average tree length reveals contraction of the networks due to tight correlation among stocks.
Research limitations – this study focuses on correlation-based network construction of MST. The scope of the study can be widened by constructing partial correlation-based MSTs and comparison of different networks structures accordingly.
Practical implications – the network properties and findings of this paper will help policymakers and regulators in setting right policies, regulatory framework, and risk management for the stock market.
Originality/Value – no previous studies have performed MST based network analysis examining macroeconomic events. Therefore, we fill the research gap and thoroughly analyse structural change and dynamics of Pakistan stock market during the turbulence of current economic crisis of Pakistan.
Keyword : stock market, minimum spanning tree, network topology, macroeconomic indicators, crisis
... The term functional connectivity has actually been coined in the context of neuroscience applications, where this approach has been used for identifying connections among brain regions showing similar electromagnetic activity patterns under specific tasks [17,18]. In parallel, much work has been devoted to establishing this concept in the fields of climatology [19,20,21,22] as well as financial markets [23,24,25]. ...
During the last years, complex network approaches have demonstrated their great potentials as versatile tools for exploring the structural as well as dynamical properties of complex systems from a variety of different fields. Among others, recent successful examples include their application to studying flow systems in both, abstract mathematical and real-world geophysical contexts. In this context, two recent developments are particularly notable: on the one hand, correlation-based functional network approaches allow inferring statistical interrelationships, for example between macroscopic regions of the Earth’s climate system, which are hidden to more classical statistical analysis techniques. On the other hand, Lagrangian flow networks provide a new tool to identify dynamically relevant structures in atmosphere, ocean or, more generally, the phase space of complex systems. This chapter summarizes these recent developments and provides some illustrative examples highlighting the application of both concepts to selected paradigmatic low-dimensional model systems.
... Stock correlations are high during financial crises, i.e., the stock prices move up or down together. This is a phenomenon in stock markets worldwide, which has drawn the attention of many scholars (Aste et al., 2010;Didier et al., 2012;Sharma and Banerjee, 2015;Kim et al., 2015;Liu and Tse, 2012). Understanding this phenomenon is very important for investors and regulators. ...
Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived from minority game for modeling stock correlations, in which an agent's expected return for one stock is influenced by the historical return of the other stock. Each agent makes a decision based on his expected return with reference to information dissemination and the historical return of the stock. We find that the returns of the stocks are positively (negatively) correlated when agents' expected returns for one stock are positively (negatively) correlated with the historical return of the other. We provide both numerical simulations and analytical studies and give explanations to stock correlations for cases with agents having either homogeneous or heterogeneous expected returns. The result still holds when other factors such as holding decisions and external events are included which broadens the practicability of the model.
... Eryiǧit and Eryiǧit (2009) investigate the correlation structure of world stock markets using the Pearson correlation-based MST and PMFG and obtain a result that agrees with Gilmore et al. (2008) that the French stock market is the most important node in both the MST and PMFG networks. Liu and Tse (2012) study the correlation structure of world stock markets during the period 2006-2010 using the dynamic Pearson CT method and find that the behavior of stock markets in different countries is synchronous. Although network approaches have produced many new well-documented descriptions of the correlation structure and evolution of world stock markets, no research has done this using a partial correlation-based network, and thus our use of the partial correlationbased MST method is a new application. ...
... Over the past decade world stock markets have experienced the US subprime crisis, the 2008 financial crisis, and the European debt crisis. This has been an unusually active period, and in our study we follow Bonanno et al. (2000), Coelho et al. (2007) and Liu and Tse (2012) and use data comprising 57 Morgan Stanley Capital International (MSCI) daily closing price indices during the period 3 January 2005-31 December 2014. As in Coelho et al. (2007) and Gilmore et al. (2008), we list indices in US dollars, reflecting the perspective of international investors and hedge-fund operators. ...
We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the
stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA
at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.
