Stock market networks: The dynamic conditional correlation approach

Lyócsa, Štefan; Výrost, Tomáš; Baumöhl, Eduard

doi:10.1016/j.physa.2012.03.038

Cited by 46 publications

(34 citation statements)

References 32 publications

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“…where (6) It should be noted that prior to the calculation of cross-lagged correlations, standardized conditional mean returns were aligned as specified in Section 2. Note however, that k may sometimes (in addition to cases described by Eq.…”

Section: The Granger Causality Testmentioning

confidence: 99%

“…The asymptotic distribution of Q(M) under the null hypothesis follows the standardized normal distribution. 6 In our empirical application, the choice of M is 5, as it corresponds to one trading week, which also has implications for the properties of the dependent variable used in the spatial regression models described in Section 3.5. Thus, this variable becomes:…”

Section: The Granger Causality Testmentioning

confidence: 99%

“…Number of empirical analyses document severe effects of the GFC on world financial markets that materialized via far reaching contagions (Caetano and Yoneyama [1]; da Silva et al [2]; Chen et al [3]; Hui and Chan [4]). It was also documented that excessive co-movements between stocks and stock market indices were associated with contagion (Dewandaru et al [5], Lyócsa et al [6]). The stock and commodity markets were in particular peril as they exhibit a long-range dependence (Lahmiri [7]).…”

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

“…Among the assets, a special attention was received by crude oil (Kang et al [27]; Liu et al [21]) due to its general economic importance. Finally, it was shown that stock market linkages and their structure vary with crisis periods (Araújo and Louçã [28], Sandoval and Franca [29], Lyócsa et al [6], Majapa and Gossel [30]). …”

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

“…Our analysis is based on the network approach that has gained currency in the econophysics literature (see for example Výrost et al [19]; Lyócsa et al [6]; Majapa and Gossel [30]; Caetano and Yoneyama [1]; Nobi et al [12]; Kuzubaş et al [14]; Majapa and Gossel [16]). Specifically, we build on Výrost et al [19] who created bi-directional 2 Granger causality networks between daily returns of developed stock markets around a world and observed that return spillovers are more probable when markets trade (in terms of trading hours) more closely to each other.…”

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

See 4 more Smart Citations

Networks of volatility spillovers among stock markets

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Physica A: Statistical Mechanics and its Applications

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We show significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity marketvolatility spillovers decrease when markets are characterized by greater temporal proximity. Terms of use: Documents inVolatility spillovers also present a high degree of interconnectedness. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.JEL-Codes: C310, C580, F010, G010, G150.Keywords: volatility spillovers, stock markets, shock transmission, Granger causality network, spatial regression, financial crisis. We benefited from helpful comments we received from two anonymous referees and participants at several presentations. Eduard Baumöhl University of Economics in Bratislava

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Section: The Granger Causality Testmentioning

confidence: 99%

Section: The Granger Causality Testmentioning

confidence: 99%

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

See 3 more Smart Citations

Networks of volatility spillovers among stock markets

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Physica A: Statistical Mechanics and its Applications

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show abstract

Identifying Dominant Economic Sectors and Stock Markets: A Social Network Mining Approach

Roy

Sarkar

2013

Lecture Notes in Computer Science

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Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering

Siudak

2023

Soc. Netw. Anal. Min.

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Cluster structure detection of the network is a basic problem of complex network analysis. This study investigates the structure of the value migration network using data from 499 stocks listed in the S&P500 as of the end of 2021. An examination is carried out whether the process of value migration creates a cluster structure in the network of companies according to economic activity. Specifically, the cohesion and segregation of the extracted modules in the network division according to (i) sector classification, (ii) community division, and (iii) network clustering decomposition are assessed. The results of this study show that the sector classification of the value migration network has a non-cohesive structure, which means that the flow of value in the financial market occurs between companies from various industries. Moreover, the divisions of the value migration network based on community detection and clustering algorithm are characterized by intra-cluster similarity between the vertices and have a strong community structure. The structure of the network division into modules corresponding to the classification of economic sectors differs significantly from the partition based on the algorithms applied.

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Stock market networks: The dynamic conditional correlation approach

Cited by 46 publications

References 32 publications

Networks of volatility spillovers among stock markets

Networks of volatility spillovers among stock markets

Identifying Dominant Economic Sectors and Stock Markets: A Social Network Mining Approach

Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering

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