The role of long‐ and short‐run correlation networks in international portfolio selection
提出一个结合长期和短期相关性网络的参数化投资组合选择模型，利用网络拓扑特征优化权重，实证显示该模型在收益、风险分散和风险调整回报方面表现良好，对监管者、管理者和投资者有参考价值。
Abstract By considering the effect of long‐ and short‐run correlation (LS) networks, we propose an LS network‐augmented parametric portfolio selection model (LSNA‐PP). First, we combine the dynamic conditional correlation‐mixed data sampling (DCC‐MIDAS) model with the planar maximally filtered graph (PMFG) method to construct LS networks and extract network topological characteristics. Second, we design portfolio weights as a function of these topological characteristics to construct the LSNA‐PP model. Third, we apply the model to construct an international portfolio from 2010 to 2021. The empirical results illustrate the efficacy of the LSNA‐PP model in two ways. First, the LSNA‐PP model clarifies the economic interpretation of topological characteristics in portfolio selection, such as the positive effect of the long‐run correlation network and the negative effect of the short‐run correlation network on the weights. Second, the LSNA‐PP model performs well in terms of return expectations, risk diversification, and attractive risk‐adjusted returns, which are especially useful for stakeholders such as regulators, managers, and investors.