1. Cascading failures in interdependent networks and financial systems -- Departmental Seminar Xuqing Huang Advisor: Prof. H. Eugene Stanley Collaborators: Prof. Shlomo Havlin Prof. Irena Vodenska Prof. Huijuan Wang Prof. Sergey Buldyrev Jianxi Gao Shuai Shao

2. Outline  Motivation  Cascading failures in interdependent networks Percolation under targeted attack Conclusion  Cascading failures in financial systems Bipartite networks model Conclusion  Future Plan

3. Motivation Cascading failure: failure of a part of a system can trigger the failure of successive parts. Financial systems. Infrastructures (power grids).

4. Motivation

5. Networks – the natural language describing interconnected system. Node, link, degree. Degree distribution:. Generating function:. e.g. Erdos-Renyi networks: Random Networks Nodes with generating function randomly connect. and size fully describe a random network. “Two random networks are the same” means “two random networks’ generating functions are the same”.

6. Motivation Percolation theory – is widely applied to study robustness and epidemic problems in complex system. Interdependent networks 1.Needed in life. 2.Until 2010, most research have been done on single networks which rarely occur in nature and technology. 3.New physics arise when interaction is considered. Analogy: Ideal gas law  Van de Waals equation

7. Outline  Motivation  Cascading failures in interdependent networks Percolation under targeted attack Conclusion  Cascading failures in financial systems Bipartite networks model Conclusion  Future Plan

8. I: Cascading failures in interdependent networks Rosatoet al Int. J. of Crit. Infrastruct. 4, 63 (2008) Blackout in Italy (28 September 2003) Power grid Communication SCADA

9. I: Cascading failures in interdependent networks Interdependent networks model: Nature 464, 1025 (2010) connectivity links （ grey) + dependency links (purple) Two types of node failure: 1.nodes disconnected from the largest cluster in one network. 2.nodes’ corresponding dependent nodes in the other network fail.

10. I: Cascading failures in interdependent networks Targeted Attack Nodes do not fail randomly in many cases ‣ Cases that low degree nodes are easier to fail 1. Highly connected hubs are secured. 2. Well-connected people in social networks are unlikely to leave the group. ‣ Cases that high degree nodes are easier to fail 1. Intentional attacks. (Cyber attack, assassination.) 2. Traffic nodes with high traffic load is easier to fail. Develop a mathematical framework for understanding the robustness of interacting networks under targeted attack.

11. I: Cascading failures in interdependent networks Targeted Attack Model

12. I: Cascading failures in interdependent networks Targeted AttackMethod Network A Targeted attack Network A’ Random failure Mapping: Find a network A’, such that the targeted attack problem on interacting networks A and B can be solved as a random failure problem on interacting networks A’ and B.

13. I: Cascading failures in interdependent networks Targeted Attack Results ER: where

14. I: Cascading failures in interdependent networks Targeted Attack Results Scale Free network: Protecting high degree nodes is not efficient to enhance the robustness of interdependent networks.

15. I: Cascading failures in interdependent networks Conclusions We tried to develop extended analytical framework of interdependent networks models with more realistic features. 1.We developed “mapping method” for calculating largest cluster and critical point of interdependent networks under targeted attack. 2.We found in interdependent network, traditional protection measures e.g. protecting high degree nodes are not efficient anymore. ( Phys. Rev. E: Rapid Communications 83, 065101 (2011) )

16. Outline  Motivation  Cascading failures in interdependent networks Percolation under targeted attack Conclusion  Cascading failures in financial systems Bipartite networks model Conclusion  Future Plan

17. II: Cascading failures in financial system Apply complex networks to model and study the systemic risk of financial systems. Btw 2000 ~ 2007: 29 banks failed. Btw 2007 ~ present: 469 banks failed.

18. II: Cascading failures in financial system Data: 1. Commercial Banks - Balance Sheet Data from Wharton Research Data Services. from 1976 to 2008 more than 7000 banks per year each bank contains 13 types of assets e.g. Loans for construction and land development, Loans secured by 1-4 family residential properties, Agriculture loans. 2. Failed Bank List from the Federal Deposit Insurance Corporation. In 2008–2011: 371 commercial banks failed.

19. II: Cascading failures in financial system Bipartite Model

20. prediction outcome fail survive reality survivefail II: Cascading failures in financial system Receiver operating characteristic(ROC) curve Results

21. II: Cascading failures in financial system Commercial real estate loans caused commercial banks failure! “commercial real estate investments do an excellent job in explaining the failures of banks that were closed during 2009 … we do not find that residential mortgage-backed securities played a significant role…” -- Journal of Financial Services Research, Forthcoming. Available at SSRN: http://ssrn.com/abstract=1644983

22. II: Cascading failures in financial system Results Sharp phase transition Stable region and unstable region

23. II: Cascading failures in financial system Conclusion: 1.Complex network model can efficiently identify the failed commercial banks in financial crisis. (capable of doing stress test). 2.Complexity of the system does contribute to the failure of banks. 3.Commercial real estate loans caused commercial banks failure during the financial crisis. 4.When parameters change, the system can be in stable or unstable regions, which might be helpful to policymakers. ( arXiv:1210.4973 [q-fin.GN] )

24. Other works and future plan  Interdependent networks theory: How clustering affects percolation? (arXiv:1205.3188) Future: Strategies to improve robustness of coupled networks, e.g. protecting node, adding links, rewire links.  Modeling financial systems: Identifying influential directors in US corporate governance network. (Phys. Rev. E 84 046101 (2011) ) Future: Similarities of investment strategies among global banks. Systemic risk, e.g. EU sovereign debt crisis, etc. Thank you!