Analysing network structures and dynamics of the Pakistan stock market across the uncertain time of global pandemic (Covid-19)

Abstract

Purpose ― The global pandemic COVID-19 has attracted considerable interest from researchers globally. However, there is very little systematic work on the impact of the COVID-19 crisis on the local stock markets. This paper proposes a complex network method that examines the effects of global pandemic COVID-19 on the Pakistan stock market to fill in these gaps. Methods ― Firstly, correlograms are plotted to inspect the correlation matrices of the overall and two sub-sample periods. Secondly, correlation threshold networks and topological properties are examined for different threshold levels. Finally, this paper uses evolving MSTs to construct a dynamical complex network and presents dynamic centrality measures, normalised tree, and average path lengths. Findings ― The findings show that COVID-19 related certainty and crisis lead to low volatility and a star-like structure, resulting in a quick flow of information and a strong correlation among the Pakistan stock market. Implication ― This analysis would help investors and regulators to manage the Pakistan stock market better. In addition, the comprehensive study solely on the Pakistan stock market will be helpful for Pakistan government officials and stock market participants to assess and predict the risks of the Pakistan stock market associated with the global pandemic COVID-19. Originality ― This paper addresses both classes of the networks. To the best of our knowledge, the static and dynamic evolution of the Pakistan stock market around the global pandemic COVID-19 has not been performed yet.

Keywords

• Stock Network
• Threshold Network
• Network topology
• Minimum Spanning Tree
• Pakistan