Dependence patterns across Gulf Arab stock markets: A copula approach

Basher, Syed Abul; Nechi, Salem; Zhu, Haiyong

doi:10.1016/j.mulfin.2014.06.008

Cited by 37 publications

(26 citation statements)

References 47 publications

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“…In terms of application of copula dependence, earlier studies, including Longin and Solnik (2001), Ang and Chen (2002), Hu (2006), Bhatti and Nguyen (2012) and Basher, Nechi, & Zhu (2014), have found asymmetric tail dependence in the markets they studied, which suggest a higher joint probability of downturn or upturn across the markets. Patton (2006b) also finds asymmetric dependence between the Deutschemark and the yen.…”

Section: Copula Models For Bivariate Distributionsmentioning

confidence: 92%

Dependence Patterns Among Banking Sectors in Asia: A Copula Approach

Mensah¹,

Premaratne

2014

SSRN Journal

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The bitter experience of the subprime crisis of 2007, the Global Financial crisis of 2008, and the extremely slow and painful ensuing recovery, has raised systemic risk to the center stage of global economic discourses. The crisis has brought home the urgent need for a thorough assessment of the dependence and interaction between banking sectors, from which most of the trouble began. This study investigates patterns and trends in absolute and tail dependence over time using daily returns for banking sectors from 12 Asian economies during the period 2000-2012. Static and time-varying Copula models, Gaussian copula, Symmetrized Joe-Clayton copulas, are employed to study the tail co-movements among the selected markets. The paper assumes a skew-t distribution for the innovation process of the marginal models. The results of the marginal models suggest strong volatility persistence in all twelve markets. There is high persistence in the absolute dependence of among market pairs. The evidence from the empirical analysis suggests that the cross-sectional average copula correlations generally remain at moderate levels with slight upward trend for all twelve markets. Correlation among the banking sectors of the advanced Asian markets economies are generally higher compared with the Emerging markets economies. The tail dependence is asymmetric across most of the market pairs; tail dependence at the lower side of the joint distributions is mostly higher than tail dependence at the upper side of the joint distributions. The results show that tail dependence is not upward trending for most of the pairs examined. However, the tail co-movements show significant spikes in response to financial stress in the global economy, which implies that there could be joint crashes in the regional banking system during extreme negative events. The fact that the region has not been the epicenter for most of the crisis periods covered in this study, yet responds significantly, makes it necessary for adoption of policies that maximize resilience to shocks. The study concludes that time-varying copulas are best suited for modeling the dependence structure of the Asian banking sector indices compared with static copula.JEL Classification: C14, F36, G10

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Section: Copula Models For Bivariate Distributionsmentioning

confidence: 92%

Dependence Patterns Among Banking Sectors in Asia: A Copula Approach

Mensah¹,

Premaratne

2014

SSRN Journal

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“…However, in our knowledge, this is the first paper to deal with the implication of energy price commodities on CO 2 emission prices by means of the dynamic SCAR copula. To the best of our knowledge, copulas have been used in commodities markets by Zohrabyan (2014) [14], Kharoubi and German(2008)[15], Reboredo (2011) [16], Nguyen and Bhatti (2012) [17], Hammoudeh et al (2013) [18] and Syed et al(2014) [19]. In addition, the returns of the series are modeled by the Generalized Autoregressive Score (GAS) model that can deal with the jumps, and occasional and temporary changes in the returns better than the GARCH-type model and thus lessens the impact of occasional extreme observations in the series.…”

Section: Introductionmentioning

confidence: 99%

Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model

Marimoutou

Soury

2015

Energy

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ADInternational audienceWe examine the dependence between the volatility of the prices of the carbon dioxide “CO2” emissions with the volatility of one of their fundamental components, the energy prices. The dependence between the returns will be approached by a particular class of copula, the SCAR (Stochastic Autoregressive) Copulas, which is a time varying copula that was first introduced by Hafner and Manner (2012) [1] in which the parameter driving the dynamic of the copula follows a stochastic autoregressive process. The standard likelihood method will be used together with EIS (Efficient Importance Sampling) method, to evaluate the integral with a large dimension in the expression of the likelihood function. The main result suggests that the dynamics of the dependence between the volatility of the CO2 emission prices and the volatility of energy returns, coal, natural gas and Brent oil prices, do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoils

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“…9 Thus, to examine the country factors that impact the dependence structure, we use average dependence coefficients, i.e., Spearman rho and Kendell tau and tail dependence as dependent variables in the Ordinary Least Squares (OLS) regressions. 10 Basher et al (2014) and Wen et al (2012) specify that Kendall's tau or Spearman's rho can capture non-linear dependencies among two random variables. 11 Basher et al (2014) pinpoint that given that financial time series generally present fat-tails pattern, we need to apply a proper method that is capable of identifying not only the linear association between variables, but also the tail dependence of the bi-variate return distribution between two assets.…”

Section: Methodsmentioning

confidence: 99%

“…10 Basher et al (2014) and Wen et al (2012) specify that Kendall's tau or Spearman's rho can capture non-linear dependencies among two random variables. 11 Basher et al (2014) pinpoint that given that financial time series generally present fat-tails pattern, we need to apply a proper method that is capable of identifying not only the linear association between variables, but also the tail dependence of the bi-variate return distribution between two assets. Therefore, following Wen et al (2012), we have Kendall's tau and tail dependence between ADR-US and ADR-home country industry indexes be the main measure of associations studied in this paper.…”

Section: Methodsmentioning

confidence: 99%