Modeling spatial and temporal dependencies among global stock markets

• We present a new spatiotemporal model and derive its maximum likelihood estimator.
• We propose a novel copula-based approach to construct the spatial weight matrix.
• We model spatial and temporal dependencies among global stock markets.
• The performance of our model is investigated using Monte Carlo experiments.
• The relative values of conditional volatilities are relevant for stock returns.

An intensive analysis of the dependence structure among stock markets is invaluable to financial experts, policy makers, and academic researchers, providing them with important implications for portfolio management, policy-making, and risk assessment. This paper proposes a novel spatiotemporal model to both examine global stock market linkages and investigate what drives stock returns. The newly introduced model allows us to go beyond conventional correlation analyses confined to studying pairwise relationships and seems to be more suitable for detecting the dependence structure of high-dimensional financial time series. Moreover, a new copula-based approach to define the spatial weight matrix is presented that is based on the construction of a dissimilarity matrix using the Spearman's contagion index. To the best of our knowledge, this paper is the first to incorporate copulas into the definition of the spatial weight matrix. In addition, the maximum likelihood estimator of our model is derived, together with a Monte Carlo simulation study evaluating its performance compared to two other methods. Finally, the results demonstrate that our proposed measure of the spatial weight matrix, coupled with our model, performs very well in terms of capturing spatial and temporal dependencies among global stock markets, and that the relative values of conditional volatilities are also important factors in determining stock returns.