Financial contagion across G10 stock markets: A study during major crises

Abstract

The multiple financial crises that occurred during the last two decades have stimulated scholars to investigate the cross‐market linkages and how shocks are transmitted across borders. Researchers usually examine financial contagion and its effects during a single period of study based on static models. This paper employs a regular vine copula specification to model the dependence dynamics across the G10 stock markets, by analysing the impacts during tranquillity periods and during each crisis episode studied apart. Empirical findings show significant differences in the connectedness, with distinctive shock transmission paths during every sub‐period. The results suggest a strong evidence of contagion during the global financial crisis and the European sovereign debt crisis. However, during the market downturn of 2002, there was no significant contagion due to the lack of market integration then. Besides, in the aftermath of the Brexit vote, Eurozone members took measures to reduce possible shock transmission and eliminate contagion.

The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide

“…In contrast, near 2011, the European debt crisis caused a rebound in systemic risk, which led to a rebound in total connectedness. The above phenomenon illustrates that the linkages among national financial cycles increase due to financial crises, policy moderation and global imbalances (see Park & Shin, 2020;BenSaïda & Litimi, 2021;Qin et al, 2021). In addition, many national financial events have a significant impact on a country or a region but have little global impact and therefore.…”

The linkage between Bitcoin and foreign exchanges in developed and emerging markets

“…Other approaches based on traditional correlations have been highly criticized due to their low performance to detect complex dependence dynamics among variables. The copula theory has the advantage of detecting the shock transmission path among variables, a desired property that multivariate GARCH models lack, as discussed by BenSaïda and Litimi ( 2021 ). Therefore, the current study prefers to model the multivariate dependence using the copula framework.…”

“…The contribution of Sklar ( 1959 ) theorem is that it separates the modeling of the marginal distributions from the copula. If the above joint cumulative distribution function F is d -times differentiable, then the joint probability distribution function ( pdf ) can be derived as follows: The pdf of the copula is: An appealing feature of copula functions is that they offer a complete separation between the marginals and the dependence structure (BenSaïda and Litimi 2021 ). The estimation of the marginals is quite straightforward and requires standard univariate techniques.…”

“…The choice of R-vine decomposition is justified by the fact that it offers a powerful environment to study the dependence structure. Moreover, the vine copula has many desirable properties compared to other copula constructions, such as flexible and wide-range dependence, flexible tail asymmetry, and flexible tail dependence, among others (BenSaïda and Litimi 2021 ).…”

“…( 2013 ), where the nodes are connected with the largest dependence as measured by Kendall’s , without any restriction on the tree structure nor on the bivariate copula family. The copula families included in this selection technique include the Independent ( ), Gaussian ( ), Student’s t ( t ), Clayton ( ), Gumbel ( ), Frank ( ), Joe ( ), Clayton-Gumbel (BB1), Joe-Hu (BB6), Joe-Clayton (BB7), Joe-Frank (BB8), Tawn type 1 ( ) and Tawn type 2 ( ) copulas, as well as their rotations by 90, 180, and 270 degrees (see, BenSaïda and Litimi 2021 , foradetailedreview).…”