Spillover Network Research Articles

Network Spillover Effects and Path Analysis of Shocks - An Empirical Study in China

Network Spillover Effects and Path Analysis of Shocks - An Empirical Study in China

Asymmetric multi-scale systemic risk spillovers across international commodity futures markets: The role of infectious disease uncertainty

This paper investigates the role of infectious disease uncertainty on multi-scale risk spillovers and portfolio implications across 12 international commodity futures markets from January 2006 to August 2022. We use wavelet packet decomposition and a novel risk spillover network topology approach based on a smooth transition vector autoregression model. The main findings are summarized as follows. First, there is an obvious asymmetry in spillover effects, i.e., the intensity of risk spillovers increases significantly during periods of high infectious disease uncertainty, and clear evidence of time-varying total spillovers across various regimes and frequencies. Second, cross-category risk spillovers are more pronounced in high-uncertainty regimes, while risk networks tend to cluster within the same category during low-uncertainty regimes. Third, the role of commodity futures in the risk spillover networks varies across different time scales and regimes, with gold consistently acting as a stable net risk transmitter. We also develop optimal portfolio strategies across commodity futures markets at different time scales and regimes based on the risk spillover analysis.

Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry

The collapse of Silicon Valley Bank in 2023 was historically significant, and based on past experiences with similar banking sector shocks, it is widely expected to trigger domino effects among tech giants and startups. However, based on the analysis of risk spillover networks established by VARs estimation, we find little evidence of such a spread of risk contagion. We observe a clear downward trend in the total connectedness index of large-cap tech companies right after the the SVB collapse. Moreover, the market quickly responded in a way that isolated the financial services subcategory within the tech sector, forming a distinct community in the network. This explains how the risk contagion paths were cut off. We also provide visualised comparisons of contagion paths within the tech network before and after the SVB’s collapse.

Climate policy uncertainty, clean energy and energy metals: A quantile time-frequency spillover study

Climate change mitigation has become a global imperative, making the development of clean energy very important. This paper explores the risk contagion effects among climate policy uncertainty (CPU), clean energy, and energy metals from the quantile time-frequency spillover perspective. The results show that: (1) The spillover effects among climate policy uncertainty, clean energy, and energy metals are nonlinear across the entire conditional distribution, showing a “V” shape, with the right-tailed risk contagion effect being more significant. (2) The risk contagion effect is abruptly enhanced by external extreme event shocks, with stronger market connectivity in the low-frequency band compared to the high-frequency band. Short-term spillover effects dominate cross-market risk contagion in the upper quantile, while long-term spillover effects dominate the lower quantile. (3) The spillover network is characterized by abrupt structural changes. That is, clean energy is a source of risk contagion at lower quantiles, while energy metals are a source of risk contagion at upper quantiles. (4) The CPU acts as an important node in the cross-market risk contagion path. Driven by CPU, the upper quantile and the long-term risk transmission is along “energy metal-clean energy” path, while the lower quantile and the short-term risk transmission is along “clean energy-energy metal” path.

Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers

Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.

Extreme risk spillovers in international energy markets: New insights from multilayer networks in the frequency domain

This paper constructs multilayer risk spillover networks in the frequency domain, containing short-, medium-, and long-term layers, to explore extreme risk spillovers in international energy markets at different frequency bands. We analyze the topology properties of multilayer networks at the system, region, and country levels. Based on daily data from 20 national energy stock markets spanning 2006–2023, the empirical results show that: (1) The overall topology of multilayer networks, including the edge structure and spillover strength structure, exhibit frequency heterogeneity. With the onset of a major crisis, the frequency heterogeneity of the network topology increases significantly, and international energy risk spillovers tend to concentrate in fewer layers (i.e., over one or two frequency bands). (2) Long-term risk is primarily responsible for the extreme risk spillover behavior between energy markets. (3) Compared to short- and medium-term spillovers, long-term spillovers are more crisis-sensitive. Furthermore, long-term spillovers within different regions vary in their sensitivity to local or global crises. (4) As market conditions fluctuate, the key players driving the short-term layer change, whereas those driving the medium- and long-term layers remain relatively stable. Overall, our findings provide new insights for policymakers and investors to prevent international energy systemic risks and optimize investment strategies.

Spillover dynamics among commodities along the Chinese oil industrial chain: From the perspective of multidimensional networks

This paper investigates the dynamic spillover effects of major commodity prices within the Chinese oil industrial chain. By integrating the Granger causality test with the BEKK-GARCH model, a multidimensional spillover network is constructed to comprehensively analyze the price spillover relationships between commodities from 2013 to 2019. The study finds that crude oil price volatility is the primary driver of spillovers to other commodities within the industrial chain, with midstream commodities such as ethylene and benzene playing a critical intermediary role in the spillover process. Additionally, the spillover between commodities is generally bidirectional, with non-closed chain spillover patterns being more common. Based on these findings, the paper offers policy recommendations to mitigate the risks of price fluctuations, promoting the stable development of the oil market and the sustained growth of the national economy.

Uncovering the Sino‐US Dynamic Risk Spillovers Effects: Evidence From Agricultural Futures Markets

ABSTRACTWith economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP‐VAR‐DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures in the United States and China from July 9, 2014, to December 31, 2022. We obtain several findings. First, CBOT corn, soybean, and wheat are identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Second, sudden events or increased economic uncertainty can enlarge the overall risk spillovers. Third, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillovers. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long‐grained rice are the two risk‐spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree.

Tail connectedness: Measuring the volatility connectedness network of equity markets during crises

This paper studies the global volatility connectedness network among 16 stock markets under different market conditions. We construct measures of tail connectedness following Ando et al. (2022) by introducing quantile regression into the classic Diebold–Yilmaz network model. We demonstrate the advantages of using tail connectedness for measuring extreme systemic risk, and examine the dynamic evolution of volatility connectedness from 2005 to 2021 at different quantiles. Our empirical results suggest that when the market is calm, the strength of volatility connectedness is determined by the closeness of economic and trade ties. Although (North) American and European stock markets tend to act as net risk providers during crises, Asian markets have become increasingly influential in the past two decades. We also find that the spillover of extreme risks is predominantly unidirectional, with either the U.S. or China sitting at the center of the spillover network and transmitting risks to the regional centers and peripheral markets.

Exploring the Spillover effects of tail risk fluctuations in the RMB exchange rate—The time-frequency and quantile connectivity perspective

This study constructs spillover indices from a volatility spillover network perspective using the Quantile Vector Autoregression (QVAR) model, capturing the RMB exchange rate's tail risk and time-frequency effects across varying shock sizes. Empirical results show that the QVAR-based spillover index more effectively captures the tail risk spillover effects across different quantiles. In the time domain, spillovers between RMB exchange rates are dynamic and particularly sensitive to extreme contingencies. In the frequency domain, RMB exchange rates demonstrate significant spillovers, primarily at low frequencies. During extreme upward events, dynamic observations show high-frequency spillovers surpassing low-frequency ones as dominant drivers in the tail spillovers of the RMB exchange rate. Additionally, the analysis of tail dependence indicators indicates a strong asymmetry in RMB exchange rate correlations, emphasizing market participants' heightened sensitivity to unfavorable shocks. These findings can serve as a reference for policymakers to strengthen risk management of the RMB exchange rate.

Interconnectedness between Islamic and conventional banks: a multilayer network view

PurposeThis study aims to examine information (stock return, volatility and extreme risk) spillovers and interconnectedness within dual-banking systems.Design/methodology/approachUsing multilayer information spillover networks, this paper conduct a deep analysis of contagion dynamics among 24 Islamic and 46 conventional banks from 2006 to 2022.FindingsThe findings show the network’s rapid response to financial shocks. Through cross-sector analysis, this paper identify information spillovers between and within Islamic and conventional banking systems. Furthermore, this research illustrates distinct roles played by Islamic and conventional banks within the multilayer network structure, contingent upon the nature of the financial shock.Practical implicationsUnderstanding the differential roles of Islamic and conventional banks in information transmission can aid policymakers and financial institutions in devising more effective risk management strategies, thereby enhancing financial stability within dual-banking systems.Originality/valueThis study contributes to the literature by emphasizing the necessity of examining contagion mechanisms beyond traditional single-layer network structures, shedding light on the shadow dynamics of information transmission in dual-banking systems.

Geopolitical Risk and Extreme Risk Connectedness Among Energy and Other Strategic Commodities: Fresh Sight Using the High‐Dimensional CoVaR Model

ABSTRACTExisting studies on commodity market risk spillovers recognize the pivotal role of geopolitical risk (GPR), but scarcely address how it drives tail risk spillover networks. This study adopts the Tail‐Event driven NETwork methodology to explore high‐dimensional Conditional Value at Risk (CoVaR) spillovers within energy and other strategic commodity markets. Our findings indicate that (1) In both lower and upper tail networks, metal and food commodities primarily act as net risk transmitters, whereas energy commodities are mainly net risk receivers. Additionally, these roles undergo short‐term reversals during periods of heightened market uncertainty. (2) There exists an asymmetrical pattern of CoVaR co‐movements in these commodity markets. The total connectedness (TC) in both the upper and lower tails demonstrates distinct responses to various extreme events. GPR tends to weaken the lower tail TC and strengthen the upper tail. (3) Incorporating GPR substantially improves the effectiveness of Minimum Connectedness Portfolio (MCoP) for these strategic commodities.

Agricultural economic security under the model of integrated agricultural industry development

As globalization and technological advancements progress, the integrated agricultural industry development model has become crucial for agricultural modernization. This model fosters inter-industry integration, enhancing agricultural competitiveness and sustainability. However, it also introduces new challenges, such as increased risk spillover and outdated regulatory mechanisms. This study employs a vector autoregression (VAR) model and a directed weighted risk spillover network for a quantitative analysis of risk dynamics within agricultural markets, identifying pathways and influencers of risk transmission across industry segments. A blockchain-based regulatory framework is proposed to improve traceability, regulatory efficiency, and data security in agricultural product management. This includes the development of registration processes, smart contracts, consensus mechanisms, and data upload protocols. The research expands the theoretical base of agricultural economic security and offers practical policy guidance, with substantial academic and practical implications. It highlights how blockchain can address market trust issues, mitigate information asymmetry, and reduce potential risks, contributing to the healthier development of agricultural economy.

Investors’ attention and network spillover for commodity market forecasting

This paper explores the role of network spillovers in commodity market forecasting and proposes a novel factor-augmented dynamic network model. We focus on a novel network definition based on investors’ attention to commodities, positing that commodities exhibit spillovers if they share a similar level of interest. To this aim, we employ Google Trends search data as an instrumental measure for attention. The results reveal that including attention-driven spillovers significantly enhances the forecasting accuracy of commodity returns.

Can local government implicit debt raise regional financial market spillover? Evidence from China

This paper investigates how local government implicit debt influences regional financial market spillovers. Utilizing the volatility spillover network of Diebold and Yilmaz (2014), we demonstrate that financial distress stemming from implicit debt can trigger regional financial market spillovers. Further analysis reveals that credit expansion amplifies the impact of local government implicit debt on cross-province volatility spillovers, while an improved financial situation lessens this impact. Moreover, this effect is more prominent in provinces with a higher foreign trade dependency and greater industrial structural change.

Does liquidity connectedness affect stock price crash risk? Evidence from China

Using a sample of CSI300 over the 2006–2021 period to establish liquidity spillover networks, we find a significantly negative relationship between liquidity connectedness and stock price crash risk. Further analysis shows that liquidity connectedness depresses stock price crash risk through two potential channels: increased conditional conservatism and decreased stock price synchronicity. Moreover, this effect is more prominent for firms with effective external monitoring, firms with lower risk-taking, and state-owned enterprises (SOEs). Overall, our paper shows that liquidity connectedness is an important factor influencing crash risk and provides useful guidance for corporate management and investor decision-making.

Analyzing dynamic patterns of information flow between bitcoin and economic uncertainty in light of public sentiments: A statistical behavior approach

Modeling and analyzing interrelationships within the Bitcoin market, as a prominent cryptocurrency, leads to understanding hidden structures, effective management and informed decision-making. Regarding this matter, numerous studies have analyzed the time-varying spillover patterns in this ecosystem. Although spillover network analysis can elucidate the nature and strength of correlations, it may not be adept at handling the conditional interdependencies within intricate non-linear and dynamic essential behaviors of financial time series. This research tries to address the mentioned challenges by presenting a novel analytical model to investigate the dynamic communication patterns among Bitcoin, United States Economic Policy Uncertainty (US EPU) and public sentiments. Following this objective, rather than directly exploring the effect of original data series on each other, the approach decomposes them into sequences of meaningful statistical behaviors, at different lag-lead horizons. Subsequently, considering the significance of conditional dependencies, we extract and analyze the rules and patterns of information flow among the observed behaviors. The findings not only unveil a distinct flow pattern compared to the spillover network, but also offer valuable insights into dynamic interactions and dominant behaviors under various scenarios. One observation suggests that as the historical range of predictors increases in predicting future changes, their effectiveness or reliability decreases, while their number simultaneously increases. Moreover, the trend slope of Bitcoin functions as a notable behavior in propagating information, directly influencing both economic uncertainty and investor sentiment. The proposed model enhances the understanding of interaction between financial time series and provides useful perspectives for analysis and risk management.

Multilayer networks for measuring interconnectedness among global stock markets through the lens of trading volume-price relationship

Using daily data spanning from 5 January 2004 to 22 November 2022, we quantify the spillover effects between 42 global stock markets. Specifically, combining causal structure learning and Elastic-Net-VAR methods, we innovatively construct multilayer causal networks based on volume-price relationship. Then, we analyze the network characteristics of multilayer spillover networks from system and market levels. Our findings indicate that there is heterogeneity in risk spillovers of price and volume networks, highlighting how trading volume spillover network play an important role in the risk contagion. Furthermore, multilayer interconnected networks confirm the risk spillovers between volume and price, and it exhibits significant differences compared to single-layer network. In addition, at system-level, each network layer shows unique network structures and dynamic evolution characteristics. At market-level, global stock markets play different roles in emitting or receiving shocks through various transmission channels. Our study emphasizes the importance of intra- and inter-layer risk propagation in multilayer networks based on volume and price, and has significant implications for developing investment strategies and global portfolio risk management.

Network spillover effects associated with the ChooseWell 365 workplace randomized controlled trial to promote healthy food choices

Food choices are closely linked to culture, social relationships, and health. Because many adults spend up to half their time at work, the workplace provides a venue for changing population health-related behaviors and norms. It is unknown whether the effects of a workplace intervention to improve health behaviors might spread beyond participating employees due to social influence. ChooseWell 365 was a randomized controlled trial testing a 12-month healthy eating intervention grounded in principles of behavioral economics. This intervention leveraged an existing cafeteria traffic-light labeling system (green = healthy; red = unhealthy) in a large hospital workplace and demonstrated significant improvements in healthy food choices by employees in the intervention vs. control group. The current study used data from over 29 million dyadic purchasing events during the trial to test whether social ties to a trial participant co-worker (n = 299 intervention, n = 302 control) influenced the workplace food choices of non-participants (n = 7900). There was robust evidence that non-participants who were socially tied to more intervention group participants made healthier workplace food purchases overall, and purchased a greater proportion of healthy (i.e., green) food and beverages, and fewer unhealthy (i.e., red) beverages and modest evidence that the benefit of being tied to intervention participants was greater than being tied to control participants. Although individual-level effect sizes were small, a range of consistent findings indicated that this light-touch intervention yielded spillover effects of healthy eating behaviors on non-participants. Results suggest that workplace healthy eating interventions could have population benefits extending beyond participants.

Untangling cross-border interfirm effects in SMEs’ working capital financing: A cyber-behavioral signaling perspective

This study analyzes the intricate dynamics of cross-border interfirm interactions in the working capital financing of small and medium-sized enterprises (SMEs) by using a novel cyber-behavioral signaling perspective. It emphasizes the pivotal role played by the generation and transmission of credible signals to mitigate informational disparities inherent in SME finance. While extant literature predominantly focuses on physical-centric signal types, our work sheds light on SMEs’ strategic use of behavioral factors in the network ecology to generate and transmit signals. Grounded in signaling theory and informed by network ecology, we propose that network ecology constitutes a fertile environment for SMEs to effectively communicate their creditworthiness through high-quality signals. Using survey data from 276 Chinese SMEs, our empirical analysis reveals that cross-border interfirm effects—both unintentional network spillover and intentional network transitivity—significantly bolster SME financing outcomes. Leveraging digital knowledge repositories mediates the strength and effectiveness of these signals, and exerts an even stronger positive impact on SME working capital financing in the absence of well-established normative mechanisms in the wider business ecosystem. By integrating a cyber-behavioral dimension into signaling theory within the SME financing domain, this research offers new understanding on how SMEs can harness the untapped power of their interconnected relationships to secure external capital. The study contributes to theory and practice by elucidating pathways for SMEs to navigate complex interfirm linkages to facilitate improved access to working capital.