The role of long‐ and short‐run correlation networks in international portfolio selection

Abstract

AbstractBy 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.