Contagion Risks and Systemic Stability in Financial Networks

An agent-based model is proposed, constructing an evolutionary banking system, where interbank loans and investment strategies are, respectively, determined by liquidity shortage and utility maximization. The causes of systemic risk are then explored based on the evolutionary banking system, which is calibrated by a sample from China. The regulatory interventions indicate the positive effects of increased investment assets, while the negative but inappreciable effects of increased interbank counterparties on contagion risks decrease. This observation hints at the possibility of promoting systemic stability by incentivizing more diversifications in investment assets instead of interbank counterparties. The results also demonstrate the advantages of prudential liquidity requirements, interbank liquidity facilities, and monetary policies from the central bank in promoting banking system stability.

Using Entropy to Evaluate the Impact of Monetary Policy Shocks on Financial Networks

We analyze the changes in the financial network built using the Dow Jones Industrial Average components following monetary policy shocks. Monetary policy shocks are measured through unexpected changes in the federal funds rate in the United States. We determine the changes in the financial networks using singular value decomposition entropy and von Neumann entropy. The results indicate that unexpected positive shocks in monetary policy shocks lead to lower entropy. The results are robust to varying the window size used to construct financial networks, though they also depend on the type of entropy used.

Understanding Changes in the Topology and Geometry of Financial Market Correlations during a Market Crash

In econophysics, the achievements of information filtering methods over the past 20 years, such as the minimal spanning tree (MST) by Mantegna and the planar maximally filtered graph (PMFG) by Tumminello et al., should be celebrated. Here, we show how one can systematically improve upon this paradigm along two separate directions. First, we used topological data analysis (TDA) to extend the notions of nodes and links in networks to faces, tetrahedrons, or k-simplices in simplicial complexes. Second, we used the Ollivier-Ricci curvature (ORC) to acquire geometric information that cannot be provided by simple information filtering. In this sense, MSTs and PMFGs are but first steps to revealing the topological backbones of financial networks. This is something that TDA can elucidate more fully, following which the ORC can help us flesh out the geometry of financial networks. We applied these two approaches to a recent stock market crash in Taiwan and found that, beyond fusions and fissions, other non-fusion/fission processes such as cavitation, annihilation, rupture, healing, and puncture might also be important. We also successfully identified neck regions that emerged during the crash, based on their negative ORCs, and performed a case study on one such neck region.