Quantifying the contagion effect of the 2008 financial crisis between the G7 countries (by GDP nominal)

Silva, Marcus Fernandes da; Pereira, Éder Johnson de Area Leão; Filho, Aloísio Machado da Silva; Castro, Arleys Pereira Nunes de; Miranda, José García Vivas; Zebende, Gilney Figueira

doi:10.1016/j.physa.2016.01.099

Cited by 68 publications

(34 citation statements)

References 38 publications

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“…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]).…”

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. Similarly, stronger return spillovers were identified also between markets which, in a given time zone, trade at similar trading hours (Al Rahahleh and Bhatti [38]).…”

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

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Networks of volatility spillovers among stock markets

Baumöhl

Kočenda

Lyócsa

et al. 2018

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: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

Networks of volatility spillovers among stock markets

Baumöhl

Kočenda

Lyócsa

et al. 2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

show abstract

“…As we intend to evaluate the evolution of correlations over time, splitting the whole sample according to the specific decade, we can calculate the difference in correlation between those decades, measuring the possible increased connection between the financial assets used. To do so, we use the ∆ρDCCA(n) proposed by [56]. For this measure, it is also possible to test its significance, aligning with the critical values of [57,58] and with the following testing hypotheses:…”

Section: Methodsmentioning

confidence: 99%

The Exposure of European Union Productive Sectors to Oil Price Changes

Ferreira

Pereira

2020

Sustainability

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Oil is one of the most important products in the world, being used for fuel production but also as an input in several industries. After the oil shocks of the 1970s, which caused great turbulence, the interest in the analysis of this particular product grew. The analysis of the comovements between oil and other assets became a hot topic. In this study, we propose an analysis of how oil price correlates with several industry indexes. The detrended cross-correlation analysis coefficient (ρDCCA) is used, with data from 1992 to 2019, and we analyze not only the correlation between oil and several Euro Stoxx indexes during the whole sample, but also how that correlation evolved for the different decades (1990s, 2000s and 2010s). Naturally, oil and gas are the sectors that correlate the most with crude oil, with correlation coefficients reaching levels higher than 0.6 in some cases. However, the results also indicate that all sectors are now more exposed to oil price variations than in the past, with the financial sector as one of the sectors with the greatest increase in correlation.Sustainability 2020, 12, 1620 2 of 16 importance for the economy as a whole, but also because price shocks are common, having an impact on the economy. Moreover, and because oil is not only a very important raw material but also a non-renewable resource, the study of its behavior, in this case the comovements of its price with stock markets, could go towards a better understanding of how financial stock markets can be related with sustainability (see, for example, [12,13] for some discussion on this topic).In this study we propose an analysis of how oil price correlates with several of the Euro Stoxx industry indexes. The objective is to understand how the correlation differs among 10 different sectorial indexes: oil and gas, basic materials, industrials, consumer goods, health care, consumer services, telecommunications, utilities, financials and technology. Despite the large body of literature analyzing comovements between oil price and other assets, we found some need of work analyzing the possible dynamics of the relationship between crude oil prices and sectoral indexes in the EU. This kind of analysis could contribute to energy policies to reduce European countries' dependence on oil.Despite the existence of several approaches to the study of correlations between oil and different sectors, we propose a different approach. Basing our study on the detrended cross-correlation analysis coefficient (ρDCCA), and with data from 1992 to 2019, we analyse not only the correlation between oil and the chosen indexes during the whole sample, but we also make an analysis for each decade, i.e., the 1990s, 2000s and 2010s. With this, and based on the difference in the correlation between different periods (∆ρDCCA), it is possible to analyze the evolution of correlations over time and understand if those indexes' exposure to possible oil shocks increased or not, something of great importance for all the agents mentioned above.Our main findings point ...

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“…In this article, we concentrate on the regional features of the financial contagion, and thus we follow the literatures [8,58,59] to adopt an undirected symmetric measure. Following the definition of Gallegati [3], contagion occurs when the cross-linkages between markets increase significantly after a financial shock.…”

Section: Contagion Test and Measurementioning

confidence: 99%

“…Following Da Silva et al [59], we adopt the increment in interdependence to measure the contagion strength (CS). If there exists contagion between the indices and in period , we represent CS as shown in formula (9).…”

Section: Contagion Test and Measurementioning

confidence: 99%

Mutual Information Based Analysis for the Distribution of Financial Contagion in Stock Markets

Wang¹,

Xia²

2017

Discrete Dynamics in Nature and Society

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This paper applies mutual information to research the distribution of financial contagion in global stock markets during the US subprime crisis. First, we symbolize the daily logarithmic stock returns based on their quantiles. Then, the mutual information of the stock indices is calculated and the block bootstrap approach is adopted to test the financial contagion. We analyze not only the contagion distribution during the entire crisis period but also its evolution over different stages by using the sliding window method. The empirical results prove the widespread existence of financial contagion and show that markets impacted by contagion tend to cluster geographically. The distribution of the contagion strength is positively skewed and leptokurtic. The average contagion strength is low at the beginning and then witnesses an uptrend. It has larger values in the middle stage and declines in the late phase of the crisis. Meanwhile, the cross-regional contagion between Europe and America is stronger than that between either America and Asia or Europe and Asia. Europe is found to be the region most deeply impacted by the contagion, whereas Asia is the least affected.

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Quantifying the contagion effect of the 2008 financial crisis between the G7 countries (by GDP nominal)

Cited by 68 publications

References 38 publications

Networks of volatility spillovers among stock markets

Networks of volatility spillovers among stock markets

The Exposure of European Union Productive Sectors to Oil Price Changes

Mutual Information Based Analysis for the Distribution of Financial Contagion in Stock Markets

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