Standardized Local Assortativity and Systemic Risk in Financial Markets

What is it about?

This study focuses on the concept of assortativity in complex networks, highlighting the limitations of using a single global measure to quantify assortative mixing. The authors propose a standardized local assortativity coefficient to capture local patterns of assortativity, which has implications for understanding network resilience, systemic risk, and predicting future network performance. The study demonstrates the value of this coefficient in social and financial networks, providing a granular level of insight and complementing other network statistics. The local assortativity coefficient may also be applicable to other fields like biology, epidemiology, and technology, given its potential to reveal local patterns that would otherwise be overlooked in complex networks.

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Why is it important?

This research is important because it highlights the limitations of using global measures to understand the assortativity of networks. The study introduces a standardized local assortativity coefficient that captures local (dis)assortative patterns and provides a more granular understanding of network heterogeneity, resilience, and systemic risk. Key Takeaways: 1. The use of a single global measure is inadequate to quantify the assortative mixing of complex networks. 2. Local patterns in networks are often smoothed out with a global measure, and a standardized local assortativity coefficient is proposed to observe granular details. 3. The standardized local assortativity coefficient provides insights into financial risks and can be used as an early warning signal for severe market downturns. 4. The coefficient complements other network statistics and may have implications for financial risks, providing a summary of both homophilic and resilient characteristics of a network. 5. The concept of local assortativity can be extended to other fields such as biology, epidemiology, and technology to account for local features in complex networks.