Therefore, regulators express a urgent need to anticipate such tipping points, and to compute early warnings of critical transitions, in order to preserve financial stability. In particular, systemic risk 6 refers to connectedness, contagion 7, cascades, and thus the diffusion of exogenous and/or endogenous shocks within and/or across financial markets 8– 12.
Similarly, financial markets exhibit critical transitions, that is sudden regime shifts that can turn into systemic risk 5. Over recent years, there has been a growing interest in early warning signals of critical transitions in complex systems, mainly in socio-ecological fields 1– 4. Our results suggest that our indicators have informational content about a future tipping point, and have therefore strong policy implications. Over the second sub-sample, used as a robustness check, our two statistical metrics also capture, to a large extent, the 2016 financial turmoil. For instance, our empirical model perfectly predicts the Global Financial Crisis. Over the first one, using receiver operating curve, we show that we are able to detect a tipping point about one year before it occurs. To capture these features, we build two early warning indicators based on the number of regime switches, and on the time between two switches. Concurrently, stock markets start to desynchronize.
Nodes rapidly switch between "being in" and "being out" the information diffusion process. Before a tipping point occurs, we observe flickering in information spreading, as measured by clustering coefficients. We base our analysis on information spreading patterns in dynamic temporal networks, where nodes are connected by short-term causality. This paper aims at building and validating a new class of early warning signals of critical transitions. As many complex dynamical systems, financial markets exhibit sudden changes or tipping points that can turn into systemic risk.
0 kommentar(er)
