Financial Market Network Research Articles

Monetary policy shocks and the high-frequency network connectedness of stock markets

We study the high-frequency impact of monetary policy decisions on the structure of financial market networks, using 1-second return data for 10 industry-level equity indices in the United States. We find that FOMC meetings and policy surprises have a significant influence on network connectedness in returns, volatility, skewness, and kurtosis. The information related to policy decisions instantly shocks and is quickly dissipated throughout the financial network. Network connectedness shocks are positively impacted by the magnitude of interest rate surprises, with asymmetric effects by sign and nonlinear effects being weak. Our results have implications for monetary policy, systemic risk, and asset pricing.

Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study

With the accelerated pace of financial globalization and the gradual increase in linkages among financial markets, correctly identifying and describing the risk spillover and network diffusion in the financial system is extremely important for the prevention and management of systemic risk. Based on this, this paper takes the equity markets of 17 countries around the world from 2007 to 2022 as the research object, measures the volatility spillover effect of global financial markets using R-Vine Copula and the DY spillover index, constructs the volatility spillover network of global financial markets, discovers the spillover and diffusion pattern of global financial market risks, and provides relevant suggestions for systemic risk management. It is found that (1) there are certain aggregation characteristics in the network diffusion of global financial market volatility spillover; (2) developed European countries such as the Netherlands, France, the UK, and Germany are at the center of the network and have a strong influence; (3) Asian countries such as China, Japan, and India are at the periphery of the network; and (4) shocks from crisis events enhance the global financial market volatility spillover effect. Based on the above findings, effective prevention of global financial market risk volatility spillover and network diffusion and reduction in systemic risk need to be carried out in two ways. First, by focusing on the financial markets of key countries in the network, such as the Netherlands, the UK, France, and Germany. The second approach is to mitigate the uneven development in global financial markets and reduce the high correlation among them.

Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement

AbstractThis study explores the critical factors affecting the prices of precious metals by using Granger causality tests through collecting financial market information and network traffic volume of Google Trend keywords and the traditional financial index. It establishes a forecasting model of precious metal price to assist the procurement of precious metals as raw materials which help to reduce material costs and control risks, thus lifting a company's profits. The results reveal that the prices of precious metals have a causality relation with their own exchange‐traded funds, US 10‐year treasury rate, and the Dow Jones Industrial Average but not Google Trends.

Construction of China’s automobile financial market network and its sustainability evaluation

“Made in China” has spread all over the world, and China has the status of “world factory”. However, the weak ability of independent innovation has affected the sustainable development of China’s manufacturing industry. The industrial revolution with digital and intelligent manufacturing as the core is coming. In the future, our development is not fast, and the key is how to be sustainable and healthy. The development of zero carbon vehicles such as intelligent transportation and electric vehicles is one of the highlands of technological competition in the transportation field, and it is also the core measure to achieve carbon peak and carbon neutralization. Identifying important firms in the automobile industry has always been a topical issue. This study used the transaction data of listed companies in China’s automobile manufacturing industry to build a complex network based on the quantitative data of enterprise development status using network modeling and the multi-attribute decision evaluation method. The relationship between the network structure of the financial market and the multi-index sustainability evaluation of enterprises were also studied. By extracting the financial information of environmental protection investment from the social responsibility reports of listed companies and analyzing the current status of target investment in the automobile manufacturing industry, the research shows that the current environmental protection investment in the automobile manufacturing industry is not strong and the government needs to increase supervision. The finding reveals three dynamic relationships for practical impact. The empirical result verifies that our method is effective and reliable. This approach can effectively overcome the effect of subjective factors on evaluation and provide sustainable evaluation strategy suggestions for investors in the automobile manufacturing industry.

Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach

We investigate the role of uncertainty in US financial information spillovers by introducing uncertainty measures into financial market networks from January 1986 to April 2020. A directed acyclic graph (DAG) approach is utilized to determine the network structure and reveal the contemporaneous causalities among financial markets and uncertainty. We empirically show that uncertainty matters in US financial information spillovers, and it behaves differently between return and volatility spillovers. News-based implied volatility (NVIX), capturing investors’ perception of future uncertainty, is an important linkage of financial returns, acting as an exogenous shock to financial volatility spillovers. A subsample analysis indicates that financial uncertainty (FU) matters before the financial crisis, and its role is replaced by NVIX after the crisis. We provide a new perspective to understand the information diffusion patterns of US financial markets, which has meaningful implications for investors who adjust their investment allocations according to spillovers and for policymakers who could rely on NVIX as an important indicator of financial contagions.

International competitiveness and currency internationalization: an application to RMB internationalization

This paper aims to explore the relation between international competitiveness and currency internationalization by employing an analytical framework and applying it to RMB internationalization. Our results reaffirm that economic size, financial market development and network externalities are keys in currency internationalization. We also find that international competitiveness is a potential determinant of currency internationalization. However, a more comprehensively and fully measured index of international competitiveness than the GCI is needed to investigate the potential relation between international competitiveness and currency internationalization. The RMB could become an international reserve currency that is larger than the Japanese yen and the British pound sterling, but still lags far behind the US dollar and the euro by 2035. Given the network externalities and historical experience of currency internationalization, however, it will be a long and bumpy process for the RMB to become an international currency, particularly an international reserve currency. Highlights Exploring the relation between international competitiveness and currency internationalization; Reaffirming economic size, financial market development and network externalities as keys in currency internationalization; International competitiveness is a potential determinant of currency internationalization; The RMB could become an international reserve currency by 2035; RMB internationalization will be a long and bumpy process.

Law of interest rate changes in financial markets based on the differential equation model of liquidity

Abstract The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

Abstract The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Law of interest rate changes in financial markets based on the differential equation model of liquidity

Abstract The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Does uncertainty matter for US financial market volatility spillovers? Empirical evidence from a nonlinear Granger causality network

ABSTRACT We investigate the role of uncertainty in US financial market volatility spillovers by using a directional financial network determined by the nonlinear Granger causality test. We find that uncertainty is an essential volatility transmission channel for financial market volatility spillovers. Financial uncertainty is the most central node in the financial market network. A subsample analysis, excluding the pandemic period, confirms the robustness of our results.

Computing a Complex Network Hierarchical Structure for Financial Market Networks on the Basis of the Hybrid Heuristic Algorithm

The recent empirical studies showed that money center networks in interbank markets are more robust and stable. Therefore, the research on layered financial networks is a key part of the systemic risk management. Various methods have been proposed in prior studies to find optimal partitioning of interbank networks into core and periphery subsets. However, these methods that have been adopted with approximation methods, in general, do not guarantee optimal bipartition. In this paper, a genetic simulated annealing algorithm is presented to detect a hierarchical structure in interbank networks as a hybrid heuristic algorithm, while its effects are also analyzed. The optimization of the error score for the core-periphery model is mathematically developed firstly as an improved expression of the optimization function, which incorporates the genetic algorithm into a simulated annealing algorithm to guarantee the optimal bipartition and to jump from a local optimization. The results of this algorithm are finally verified by empirical analysis of interbank networks; and, through the immunity strategy under the risk diffusion model, the significance of core-periphery structure to risk management is verified.

Graph Motif Entropy for Understanding Time-Evolving Networks.

The structure of networks can be efficiently represented using motifs, which are those subgraphs that recur most frequently. One route to understanding the motif structure of a network is to study the distribution of subgraphs using statistical mechanics. In this article, we address the use of motifs as network primitives using the cluster expansion from statistical physics. By mapping the network motifs to clusters in the gas model, we derive the partition function for a network, and this allows us to calculate global thermodynamic quantities, such as energy and entropy. We present analytical expressions for the number of certain types of motifs, and compute their associated entropy. We conduct numerical experiments for synthetic and real-world data sets and evaluate the qualitative and quantitative characterizations of the motif entropy derived from the partition function. We find that the motif entropy for real-world networks, such as financial stock market networks, is sensitive to the variance in network structure. This is in line with recent evidence that network motifs can be regarded as basic elements with well-defined information-processing functions.

Untying Interconnectedness: Topology, Stability and the Post-crisis Reforms

‘Interconnectedness’ was considered to be a cause of the 2008 financial crisis, stimulating a number of studies into the topology of financial markets. Yet the analysis of instability within networks has tended to focus on a type of ‘contagion’ which imagines serial insolvencies, with non-performance of due obligations causing solvency issues for connected institutions. A more realistic assessment of the 2008 crisis was that it was due to a drying-up of available cash. A taxonomy of contagion is proposed, and the illiquidity model of contagion is then analyzed with reference to the observed core-periphery structure of financial market networks. Finally, the post-crisis reforms are judged against the view of ‘interconnectedness’ which emerges.

Asset allocation: new evidence through network approaches

The main contribution of the paper is to employ the financial market network as a useful tool to improve the portfolio selection process, where nodes indicate securities and edges capture the dependence structure of the system. Three different methods are proposed in order to extract the dependence structure between assets in a network context. Starting from this modified structure, we formulate and then we solve the asset allocation problem. We find that the portfolios obtained through a network-based approach are composed mainly of peripheral assets, which are poorly connected with the others. These portfolios, in the majority of cases, are characterized by an higher trade-off between performance and risk with respect to the traditional Global Minimum Variance (GMV) portfolio. Additionally, this methodology benefits of a graphical visualization of the selected portfolio directly over the graphic layout of the network, which helps in improving our understanding of the optimal strategy.

Systemic Risk Indicators Based on Nonlinear PolyModel

The global financial market has become extremely interconnected as it demonstrates strong nonlinear contagion in times of crisis. As a result, it is necessary to measure financial systemic risk in a comprehensive and nonlinear approach. By establishing a large set of risk factors as the main bones of the financial market network and applying nonlinear factor analysis in the form of so-called PolyModel, this paper proposes two systemic risk indicators that can prognosticate the advent and trace the development of financial crises. Through financial network analysis, theoretical simulation, empirical data analysis and final validation, we argue that the indicators suggested in this paper are proved to be very effective in forecasting and tracing the financial crises from 1998 to 2017. The economic benefit of the indicator is evidenced by the enhancement of a protective put/covered call strategy on major stock markets.

A network approach to portfolio selection

Abstract In this study, a financial market is conceived as a network where the securities are nodes and the links account for returns' correlations. We theoretically prove the negative relationship between the centrality of assets in this financial market network and their optimal weights under the Markowitz framework. Therefore, optimal portfolios overweight low-central securities to avoid the large variances that result when highly influential stocks are included in the investor's opportunity set. Next, we empirically investigate the major financial and market determinants of stock's centralities. The evidence indicates that highly central nodes tend to coincide with older, larger-cap, cheaper and financially riskier securities. Finally, we explore by means of in-sample and out-of-sample analysis the extent to which the structure of the stock market network can be employed to improve the portfolio selection process. We propose a network-based investment strategy that outperforms well-known benchmarks while presenting positive and significant Carhart alphas. The major contribution of the paper is to employ the financial market network as a useful device to improve the portfolio selection process by targeting a group of assets according to their centrality.

Concentric network symmetry

Quantification of symmetries in complex networks is typically done globally in terms of automorphisms. Extending previous methods to locally assess the symmetry of nodes is not straightforward. Here we present a new framework to quantify the symmetries around nodes, which we call connectivity patterns. We develop two topological transformations that allow a concise characterization of the different types of symmetry appearing on networks and apply these concepts to six network models, namely the Erdős–Rényi, Barabási–Albert, random geometric graph, Waxman, Voronoi and rewired Voronoi. Real-world networks, namely the scientific areas of Wikipedia, the world-wide airport network and the street networks of Oldenburg and San Joaquin, are also analyzed in terms of the proposed symmetry measurements. Several interesting results emerge from this analysis, including the high symmetry exhibited by the Erdős–Rényi model. Additionally, we found that the proposed measurements present low correlation with other traditional metrics, such as node degree and betweenness centrality. Principal component analysis is used to combine all the results, revealing that the concepts presented here have substantial potential to also characterize networks at a global scale. We also provide a real-world application to the financial market network.

Network Analysis of the EU Insurance Sector

This paper contains an analysis of the network of the 29 largest European insurance groups and their financial counterparties. Insurance companies have direct exposures to other insurers, banks and other financial institutions through the holdings of debt, equity and other financial instruments. These exposures can cause direct contagion and thereby the spread of systemic risks. This analysis focuses on direct linkages between EU insurers and banks. Sectoral data show that at least 20% of insurers’ assets are investments in banks. As a result insurers are an important source of funding for banks. This paper adds to the expanding research on financial market networks and on systemic risks in the insurance sector. This paper considers these 29 insurers’ top ten exposures for each instrument. They represent about 10% of their total assets, which indicates a low level of concentration. More than half of the reported exposures result from investments in bonds issued by banks. In addition, some insurers have a higher exposure to banks within their own financial conglomerate. Other exposures reported include securities lending transactions and repos (both mostly collateralised with cash), as well as interest rate swaps. The network of insurance groups, banks and other financial institutions displays low interconnectivity overall, compared for instance to the interactions of the largest EU banks alone. The density of the network is relatively low. The characteristics of this network illustrate that credit and funding events cannot be expected to spread easily through direct contagion. The network shows a core-periphery structure, which partly results from the scope of the data collection: only 29 insurance groups reported exposures, and thus they form the core, while institutions from which no information was collected are in the periphery. However, the systemic importance of a few insurance groups stands out. These groups show higher levels of connectivity, proximity to credit events within the network, and importance for financial flows. Network measures for each of these aspects refer to the criteria of interconnectedness and substitutability, which are well-known in the policy debate on the systemic relevance of financial institutions. While the particular form of institutional importance varies significantly across insurance groups, the central role of a few “champions” in this network may require supervisory attention. Size, in terms of total assets and issued capital is an important factor but not the only one, determiningan insurer’s centrality in the network. Measures of the centrality of the banking counterparties also show a positive, non-linear relationship with the size of the banks. Distress in the network, as expected, causes only limited direct contagion. Insurers’ solvency positions are sufficiently large and their concentration of exposures is sufficiently low as to avoid direct contagion from a default of one of their counterparties, ultimately leading to their own default. This is also true for simultaneous distress at their top ten banking counterparties, with the exception of two insurance groups. The analysis presented is partial, as it relies only on the information collected. In particular, the analysis would benefit from including the exposures of banks and other financial institutions to insurers. This could be a useful enhancement for consideration in the future. JEL Classification: L14, G18

The stability of financial market networks

We investigate the stability of a financial market network by measuring its topological robustness, namely the ability of the network to resist structural or topological changes. The closing prices of 710 stocks in the Shanghai Stock Exchange (SSE) from 2005 to 2011 are chosen as the empirical data. We divide the period into three sub-periods: before, during, and after the US sub-prime crisis. By monitoring the size of the clusters which fall apart from the network after removing the nodes (i.e., the listed companies in the SSE), we find that: i) the SSE network is sensitive to the nodes' failure, which implies that the network is unstable. ii) the SSE network before the financial crisis has the strongest robustness against the intentional topological damage; iii) the hubs (i.e., highly connected nodes) connect with each other directly and play a vital important role in maintaining SSE network's stability.

Dynamics of cluster structures in a financial market network

In the course of recent fifteen years the network analysis has become a powerful tool for studying financial markets. In this work we analyze stock markets of the USA and Sweden. We study cluster structures of a market network constructed from a correlation matrix of returns of the stocks traded in each of these markets. Such cluster structures are obtained by means of the P-Median Problem (PMP) whose objective is to maximize the total correlation between a set of stocks called medians of size p and other stocks. Every cluster structure is an undirected disconnected weighted graph in which every connected component (cluster) is a star, or a tree with one central node (called a median) and several leaf nodes connected with the median by weighted edges. Our main observation is that in non-crisis periods of time cluster structures change more chaotically, while during crises they show more stable behavior and fewer changes. Thus an increasing stability of a market graph cluster structure obtained via the PMP could be used as an indicator of a coming crisis.