Black swan dynamics: a network-based framework for systemic risk detection and mitigation

This research offers an integrated approach that systematically unifies three proven methodologies—complex network theory, extreme value statistics, and computational linguistics—to better monitor systemic risk in financial markets. Although each methodology has been used separately in financial risk research, their systematic integration makes it possible to induce interaction effects between methodologies that are not apparent when one uses a single-method approach. We build the systemic vulnerability index (SVI), an integrated index that combines network fragility indicators, tail risk interdependencies, and sentiment-based behavioural indicators. Three large financial shocks (the 2008 financial crisis, the 2010 Flash Crash, and the 2020 COVID-19 market crash) deliver empirical evidence that the combined framework delivers risk elevation signals with lead times of 1–5 months prior to systemic events. The SVI possesses an area under the ROC curve (AUROC) of 0.83 at one-month horizons, a statistically significant margin above single measures of risk: VIX (AUROC: 0.71), SRISK (0.75), and financial stress index (0.74). The architecture reveals cyclical patterns linked to the amplification mechanisms creating systemic vulnerabilities but leaves the precise timing of events precipitating these vulnerabilities uncertain. We improve the risk detection approach by adding a stratified response protocol that can adjust policy intervention based on composite vulnerability indicators. The combined methodology shows that whilst black swan events possess inherent timing uncertainty, their systemic amplification channels exhibit characteristic precursory features.