Modeling spatial and temporal dependencies among global stock markets
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Spatial dependence,Serial correlation,Stock returns,Monte Carlo simulation
论文评审过程：Received 17 May 2014, Revised 1 September 2015, Accepted 2 September 2015, Available online 10 September 2015, Version of Record 20 October 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.09.002
