Measuring and Testing Tail Dependence and Contagion Risk Between Major Stock Markets

Su, EnDer

doi:10.1007/s10614-016-9587-y

Cited by 11 publications

(4 citation statements)

References 49 publications

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“…Lastly, this paper also takes a closer look in this market by the Pearson, VAR, and SVAR Granger causality employed by Zhang et al (2010), Shabri Abd. Majid et al (2009), Ding (2010), Tudor (2011), Vinh (2014), Su (2017) for an explanation of spillover effects, to understand which coin influences another one. We will present the basic framework of our methodologies to use in the following sections.…”

Section: Methodsmentioning

confidence: 99%

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Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

Huynh

2019

JRFM

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This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Student’s-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to ‘bad news’ and moving patterns in order to make timely decisions on three types (buy, hold, and sell).

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Section: Methodsmentioning

confidence: 99%

“…Additionally, Ding 2010also checked the co-movements with Granger causality for the US and the Asia Pacific stock markets, while Central and Eastern Europe are the scope of study by Tudor (2011). Then, there is also a country research, employing this methodology of Vinh (2014) and Su (2017).…”

Section: General Spillover Risksmentioning

confidence: 99%

Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

Huynh

2019

JRFM

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“…The first consists of traditional methods such as correlation coefficients [42], co-integration tests [43], Granger-causality tests [44], vector autoregressive models [45], and generalized autoregressive conditional heteroskedasticity (GARCH) models [46]. The second comprises methods based on copula models [14,[47][48][49][50][51][52][53]. In comparison to the traditional methods, the second type of methods can well capture the time-varying, asymmetric, and nonlinear tail dependence structure between markets, which arises from fat tails and heteroskedasticity.…”

Section: Literature Reviewmentioning

confidence: 99%

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Hu,

Zhu,

Zhang

et al. 2024

PLoS ONE

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The linkages between the US and China, the world’s two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

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“…They claim that the US market has more influence on the jump intensity of other world markets. Su (2017) examines jumps in the context of tail dependence. Chollete et al (2009) find support for asymmetric dependence in G5 and Latin American stock returns.…”

Section: Related Prior Workmentioning

confidence: 99%

Volatility and asymmetric dependence in Central and East European stock markets

Joseph

Mobarek

et al. 2020

Rev Quant Finan Acc

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We study the effects of contagion around the global financial crisis (GFC) and the Eurozone crisis periods using German and UK returns, each paired with returns from Central and East European (CEE) stock markets that recently joined the European Union (EU). Using bivariate vector error-correction models (VECMs) estimated in GARCH(1,1), we find strong support for long-run equilibrium conditions. This finding suggests that tests of tail dependence using differenced VARs may be mis-specified when long-run equilibrium conditions apply. Past news has more persistence on current volatility in CEE markets than in the developed markets. Past volatility has more persistence in the developed markets compared to the CEE markets. The T-V symmetrized Joe-Clayton (T-V SJC) copula outperforms all other copulas in goodness-of-fit, including, the T-V Gaussian and Student t copulas. This result is supported by a differenced VAR-GARCH (1,1). For CEE and developed market returns, no more than half of our market pairs exhibit significant increases in lower tail dependence, under the T-V SJC copula. Given the number of paired comparisons, the evidence on joint extreme dependence is weak. As such, CEE stock markets experienced little contagion effects during the GFC and Eurozone crisis periods, contrary to prior results. We find that the legal environment negatively impacts financial development, perhaps causing CEE and the EU markets to be isolated.

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Measuring and Testing Tail Dependence and Contagion Risk Between Major Stock Markets

Cited by 11 publications

References 49 publications

Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Volatility and asymmetric dependence in Central and East European stock markets

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