Network-Based Modeling and Analysis of Systemic Risks in Banking Systems

Preventing financial crisis has become the concerns of average citizens all over the world and the aspirations of academics from disciplines outside finance. In many ways, better management of financial risks can be achieved by more effective use of information in financial institutions. In this paper, we developed a network-based framework for modeling and analyzing systemic risks in banking systems by viewing the interactive relationships among banks as a financial network. Our research method integrates business intelligence (BI) and simulation techniques, leading to three main research contributions in this paper. First, by observing techniques such as the HITS algorithm used in estimating relative importance of web pages, we discover a network-based analytical principle called the Correlative Rank-In-Network Principle (CRINP), which can guide an analytical process for estimating relative importance of nodes in many types of networks beyond web pages. Second, based on the CRINP principle, we develop a novel risk estimation algorithm for understanding relative financial risks in a banking network called Link-Aware Systemic Estimation of Risks (LASER) for purposes of reducing systemic risks. To validate the LASER approach, we evaluate the merits of the LASER by comparing it with conventional approaches such as Capital Asset Ratio and Loan to Asset Ratio as well as simulating the effect of capital injection guided by the LASER algorithm. The simulation results show that LASER significantly outperforms the two conventional approaches in both predicting and preventing possible contagious bank failures. Third, we developed a novel method for effectively modeling one major source of bank systemic risk - correlated financial asset portfolios – as banking network links. Another innovative aspect of our research is the simulation of systemic risk scenarios is based on real-world data from Call Reports in the U.S. In those scenarios, we observe that the U.S. banking system can sustain mild simulated economic shocks until the magnitude of the shock reaches a threshold. We suggest our framework can provide researchers new methods and insights in developing theories about bank systemic risk. The BI algorithm - LASER, offers financial regulators and other stakeholders a set of effective tools for identifying systemic risk in the banking system and supporting decision making in systemic risk mitigation