Detection of Information Flow in Major International Financial Markets by Interactivity Network Analysis

Abstract

This paper is an empirical assessment of various aspects regarding interconnections among the largest world stock markets. The study investigates the dynamic connection structure of ten major stock markets using partial directed coherence (PDC) and graphical models. Recently, PDC has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. It constitutes a powerful tool to explore the causal influence between complex multivariate processes in neuroscience area. The paper proposes using this tool to study the information flow and causal influence between international financial markets. The novel proposed approach offers many advantages and superiorities compared to the conventional methodologies for investigating financial links across stock markets. Applying PDC to ten opening and closing major stock index data allows the discrimination between directed and induced causality connections between financial markets. The graphical model summarizing the PDC results and instantaneous causality analysis shows the US, UK, Germany and Hong Kong have a consistently strong causality impact on price movements in major financial markets. The strong connection between the ten major financial markets supported by the empirical results tends to reduce the gain of international diversification and intensifies the risk of the international financial system instability.