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Detecting capital market convergence clubs
(Walter de Gruyter GmbH, 2017) Beylunioglu, F.C.; Stengos, T.; Yazgan, M.E.
In this study, we propose a new method to find convergence clubs that combine pairwise method of testing convergence with maximal clique algorithm. Unlike many o those already developed in the literature, this new method aims to find convergence clubs endogenously without depending on priori classifications. We use our method to study convergence among different capital markets as captured by their respective indices. Stock market convergence would indicate the absence of arbitrage opportunities in moving between the different markets as they would all present investors with similar risks. Furthermore, stock market convergence would be a precursor to GDP convergence as these economies would be bound by similar (possibly unobservable) common factors that affect long run macroeconomic performance. © 2017 Walter de Gruyter GmbH, Berlin/Boston.