Explaining Stock Market Correlation: A Gravity Model Approach

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The Dark Side of Global Integration: Increasing Tail Dependence (2010, unknown): “Given our panel data approach and the inclusion of cross-section fixed effects, we are able to account for the influence of time-constant factors such as geographical distance between markets (Flavin et al. (2002)) or language similarity (ref).”
Liberalisation and stock market co-movement between emerging economies (2011, methodology): “Several factors of this type have been found to influence cross-market correlations, such as geographical distances, common borders, and synchronicity in trading hours (Flavin et al. 2002).”
A spatial analysis of international stock market linkages (2013, background): “…gravity models, where the correlation or coexceedance among national stock markets is regressed on economic sizes (GDP or market capitalization) and distances (measured by cross-country-specific variables) between the markets (see e.g. Flavin et al. 2002, Beine and Candelon 2010, and Wälti 2011).”
Visualizing data as objects by DC (difference of convex) optimization (2017, background): “The statistical value ωi relates to the importance of market i relative to the world market portfolio, [25], and the dissimilarity δij is based on the correlation between markets i and j, [5].”