Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework

Zhang, Weiping; Xin-tian, Zhuang; Yang, Lu; Wang, Jian

doi:10.1016/j.irfa.2020.101454

Cited by 54 publications

(32 citation statements)

References 50 publications

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“…ij is the element of row i and column j of matrix C p in Equations (5) and (6). On the premise that {ε t } has no correlation, we can use the second central moments expressed in the following terms:…”

Section: Generalized Variance Decompositionmentioning

confidence: 99%

“…All results support Hypothesis 5. Unlike the studies of Zhang et al [5], which identify a positive link between capital liquidity and systematic risk, our model differentiates between risk absorption and risk spillover while taking into account relational data and network endogeneity which could map the specific condition of FDI influence. In terms of FPI, whether significant or not, the parameters of foreign portfolio receiver and sender are all positive except in the recovery period, implying that FPI significantly amplifies both risk spillover and absorption.…”

Section: Factors Affecting the Weighted Connectedness Networkmentioning

confidence: 99%

“…Especially the recent financial crises have highlighted the role of market connectedness in triggering systemic risk [4]. An in-depth analysis of market connectedness (e.g., volatility) and its channels is of great significance in managing systemic risks and maintaining financial stability, thus contributing to the smooth functioning of the real economy in turn [5]. In this context, this study establishes a holistic framework, encompassing volatility spillover analyzing framework and the recent development of graph dependence theory, to seek a better understanding of market connectedness from a dynamic perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Mensi et al [18] built volatility spillover networks of global, regional and GIPSI stock market by drawing on the Diebold and Yilmaz [14] methodology. Zhang et al [5] applied GARCH-BEKK model to construct volatility networks of the G20 stock market. These studies demonstrate the feasibility and versatility of econometric-based networks in capturing financial network topology, volatility spillover, and risk contagion mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…Applying the same method in the G20 stock network, Zhang et al [1] found that government debt and inflation are positively correlated with spillover risk. Most of the existing literature uses linear regression, ordinary panel regression, and spatial autoregressive models, which do not deal with the correlation problem embedded in relational data [5]. One exception is a recent analysis by Zhang et al [5], which used the QAP method (quadratic assignment procedure) to exploit the relevance of relational data in explaining volatility spillover.…”

Section: Introductionmentioning

confidence: 99%

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A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

Zhang

Chen

2020

Sustainability

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The financial penetration accelerated by economic globalization and financial liberalization has inevitably induced market co-movement and the rising likelihood of cross-market risk contagion. An in-depth analysis concerning the carrier of risk contagion, i.e., market connectedness network, is of great significance for risk management. This study aims to establish a holistic framework to shed light on the topological dynamics and the evolving channels of connectedness network among 24 major stock markets in two aspects; namely, a dynamic perspective juxtaposing crisis and non-crisis periods, and a contrasting perspective between risk absorption and risk spillover. To this end, a methodological framework of the generalized variance decomposition and generalized exponential random graph models (GERGM) is constructed, in which the former method formulates the asymmetric causal relationships of stock return volatility among countries and regions into weighted and directed networks, and the latter method simulates and models the varying attributes of different contagion channels in the formation of tie directions and weights. The results indicate that the global stock market network reflects typical event-driven and time-varying characteristics. Countries and regions that rely heavily on foreign direct investment (FDI) are more likely to absorb risks, especially during the post-crisis recovery period, while countries and regions with higher foreign portfolio holdings are more inclined to risk spillover, especially during the subprime crisis. Geographical proximity and bilateral trade volume amplify risk contagion, whereas foreign exchange reserve holding improves robustness. This holistic framework allows the identification of the direction and intensity of risk contagion and the clarification of priority of risk transmission channels in different stages, thus reducing the uncertainty of risk management and providing insights into the macro-prudential managements toward sustainable economic development.

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Section: Generalized Variance Decompositionmentioning

confidence: 99%

Section: Factors Affecting the Weighted Connectedness Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

Zhang

Chen

2020

Sustainability

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Volatility Transmission Role of Indian Equity and Commodity Markets

Kaur,

Chaudhary

2024

Deep Learning Tools for Predicting Stock Market Movements

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A network analysis of electricity demand and the cryptocurrency markets

Okorie

2020

Int. J Fin Econ

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This article examines the connectedness and information spillover in the Electricity‐Crypto Network (ECN) system. The Bitcoin and Ethereum markets are studied due to the level of electricity demand for active trading and mining in the three leading crypto mining economies (United States, China, and Japan). Among other findings, the leading net transmitter of information is the return of the Bitcoin market while the demand for electricity in the U.S. and Japan are the leading net information receivers in the ECN system. In a nutshell, the return and trading volumes of the cryptocurrency markets are net information transmitters while the markets' volatility and the demand for electricity in the U.S., China, and Japan are net information receivers in the system. As a policy relevance, given the favourable developments in these crypto markets, greener sources of electrical energy are expedient to mitigate emissions while mining these coins. This will reduce the impact of human activities on the climate.

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Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework

Cited by 54 publications

References 50 publications

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

Volatility Transmission Role of Indian Equity and Commodity Markets

A network analysis of electricity demand and the cryptocurrency markets

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