Systemic Risk Analysis on Reconstructed Economic and Financial Networks.

How the ownership structures cause epidemics in financial markets: A network-based simulation model

Financial and economic networks - an overview, Anna Nagurney: background financial networks economic networks the Internet and new directions outline of the volume notes. Part 1 Financial networks: on structural properties of the market graph, Vladimir Boginski, Sergiy Butenko and Panos M. Paradalos multistage stochastic mean-variance portfolio analysis with transaction costs, Nalan Gulpniar, Berc Rusetm and Reuben Settergren a stochastic network approach for integrating pension and corporate financial planning, John M. Mulvey, Koray D. Simek and Bill Pauling variational inequalities for evolutionary financial equilibrium, Patrizia Daniele the growing importance of networks in finance and its effects on competition, Stijn Claessens et al international financial networks with electronic transactions, Anna Nagurney and Jose Cruz using financial options to hedge transporation capacity in a deregulated rail industry, Stephen M. Law, Alexandra E. MacKay and James F. Nolan. Part 2 Economic networks: a supply chain network economy - modeling and qualitative analysis, Ding Zhang, June Dong and Anna Nagurney applications of fluid modelling in distribution systems, Soulaymane Kachani and Georgia Perakis an agent-based evolutionary trade network stimulation, Floortje Alkemade et al evolution of worker-employer networks and behaviors under alternative non-employment benefits - an agent-based computational study, Mark Pingle and Leigh Tesfatsion capacity provision networks - a technology framework and economic analysis fo Web cache trading hubs, Xianjun gent et al.

As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model to describe the risk propagation process during crises. We empirically test the model with 2007 US commercial banks balance sheet data and compare the model prediction of the failed banks with the real failed banks after 2007. We find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation. The results suggest that this model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008–2011.

Spectral signatures of structural change in financial networks

Financial risk contagion based on dynamic multi-layer network between banks and firms

In this article, a capacity expansion framework is proposed for failure-prone flow-networks. A systemic risk measure that quantifies the risk of unsatisfied demand due to cascaded edge failures is considered. To minimize the total cost of additional edge capacities, while keeping the risk of unsatisfied demand below a certain threshold, a general stochastic optimization problem is formulated. The distribution of unsatisfied demand is calculated via Monte-Carlo simulations embodied within a grid search algorithm that identifies the feasible region. Thereafter, the cost-optimal edge capacity expansion plan is computed by a differential evolution algorithm. Contributions of this article are: 1) consideration of both immediate investment and future risk costs of capacity expansion plans; 2) a generic flow-network model that can be tuned for different real-life applications; 3) addressing the stochastic nature of both supply and demand simultaneously within a systemic risk framework; 4) use of eigenvector centrality for edge grouping in systemic risk analysis. An extensive numerical study is performed to investigate the effects of different edge grouping methods, characteristics of stochastic components, and cost parameters on the feasible region and optimal solution. The proposed framework is also demonstrated on a case study adapted from ERCOT 13-bus test system.

Systemic Risk Contribution from Financial Network in the UK

Determinants of risk in the banking sector during the European Financial Crisis

This study aims to measure systemic risk in conventional commercial banks in Indonesia with the Conditional Value at Risk (CoVaR) model developed by Adrian and Brunnermeier (2009). The financial crises of 1998 and 2008 have become valuable lessons for Indonesian banks to always maintain financial system stability because the impact caused by systemic risks is very large. Systemic risk is the possibility of instability due to contagion in some or all of the financial system. This study uses a sample of 6 conventional commercial banks in Indonesia from January 2012 to December 2018. The results obtained from this study is the systemic risk is not related to the size of banks. Each bank has a negative externality so that it can cause a bank rush and systemic impact.

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

This paper examines the dependence of network performance measures on network size and considers scaling results for large networks. We connect two performance measures that are well studied, but appear to be unrelated. The first measure is concerned with energy metrics, namely the $\Hcal_2$--norm of a network, which arises in control theory applications. The second measure is concerned with the notion of "tail risk" which arises in economic and financial networks. We study the question of why such performance measures may deteriorate at a faster rate than the growth rate of the network. We first focus on the energy metric and its well known connection to controllability Gramian of the underlying dynamical system. We show that undirected networks exhibit the most graceful energy growth rates as network size grows. This rate is quantified completely by the proximity of spectral radius to unity or distance to instability. In contrast, we show that the simple characterization of energy in terms of network spectrum does not exist for directed networks. We demonstrate that, for any fixed distance to instability, energy of a directed network can grow at an exponentially faster rate. We provide general methods for manipulating networks to reduce energy. In particular, we prove that certain operations that increase the symmetry in a network cannot increase energy (in an order sense). Secondly, we focus on tail risk in economic and financial networks. In contrast to $\Hcal_2$--norm which arises from computing the expectation of energy in the network, tail risk focuses on tail probability behavior of network variables. Although the two measures differ substantially we show that they are precisely connected through the system Gramian. This surprising result explains why topology considerations rather than specific performance measures dictate the large scale behavior of networks.

The complex web of exposures and interlinkages across the financial system highlights the relevance of network analysis in understanding systemic risk and guiding the design of financial regulation. This paper discusses how network models—and those based on epidemiological approaches in particular—offer a compelling description of the structure of real-world financial systems and shed light on different contagion mechanisms seen during the global financial crisis. We also review how these insights may inform macroprudential risk assessment and policy in the areas of stress-testing the financial system and the regulation of systemically important institutions. The role of non-bank financial intermediation and social networks in shaping financial system risk is also briefly considered.

Macro-Prudential Regulation from the Perspective of the Financial Network

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

Shadow Banks and Systemic Risks