36 research outputs found
Network theory and its applications in economic systems
This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems.
In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are
protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc\ = 0, coupled SF networks are significantly more vulnerable with pc\ significantly larger than zero. 2) We analytically demonstrate that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system.
In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on
common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks 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
Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation
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.Comment: 13 pages, 7 figure
Robustness of interdependent networks under targeted attack
When an initial failure of nodes occurs in interdependent networks, a cascade
of failure between the networks occurs. Earlier studies focused on random
initial failures. Here we study the robustness of interdependent networks under
targeted attack on high or low degree nodes. We introduce a general technique
and show that the {\it targeted-attack} problem in interdependent networks can
be mapped to the {\it random-attack} problem in a transformed pair of
interdependent networks. We find that when the highly connected nodes are
protected and have lower probability to fail, in contrast to single scale free
(SF) networks where the percolation threshold , coupled SF networks are
significantly more vulnerable with significantly larger than zero. The
result implies that interdependent networks are difficult to defend by
strategies such as protecting the high degree nodes that have been found useful
to significantly improve robustness of single networks.Comment: 11 pages, 2 figure
Robustness of a partially interdependent network formed of clustered networks
Clustering, or transitivity, a behavior observed in real-world networks, affects network structure and function. This property has been studied extensively, but most of this research has been limited to clustering in single networks. The effect of clustering on the robustness of coupled networks, on the other hand, has received much less attention. Only the case of a pair of fully coupled networks with clustering has recently received study. Here we generalize the study of clustering of a fully coupled pair of networks and apply it to a partially interdependent network of networks with clustering within the network components. We show, both analytically and numerically, how clustering within networks affects the percolation properties of interdependent networks, including the percolation threshold, the size of the giant component, and the critical coupling point at which the first-order phase transition changes to a second-order phase transition as the coupling between the networks is reduced. We study two types of clustering, one proposed by Newman [Phys. Rev. Lett. 103, 058701 (2009)] in which the average degree is kept constant while the clustering is changed, and the other by Hackett et al. [Phys. Rev. E 83, 056107 (2011)] in which the degree distribution is kept constant. The first type of clustering is studied both analytically and numerically, and the second is studied numerically
The robustness of interdependent clustered networks
It was recently found that cascading failures can cause the abrupt breakdown
of a system of interdependent networks. Using the percolation method developed
for single clustered networks by Newman [Phys. Rev. Lett. {\bf 103}, 058701
(2009)], we develop an analytical method for studying how clustering within the
networks of a system of interdependent networks affects the system's
robustness. We find that clustering significantly increases the vulnerability
of the system, which is represented by the increased value of the percolation
threshold in interdependent networks.Comment: 6 pages, 6 figure
Partial correlation analysis: applications for financial markets
The presence of significant cross-correlations between the synchronous time evolution of a pair of
equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate
the level of similarity in the price changes for a given pair of stocks, but it does not measure whether
other stocks influence the relationship between them. To explore the influence of a third stock on the
relationship between two stocks, we use a partial correlation measurement to determine the underlying
relationships between financial assets. Building on previous work, we present a statistically robust
approach to extract the underlying relationships between stocks from four different financial markets:
the United States, the United Kingdom, Japan, and India. This methodology provides new insights into
financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the
capabilities of this methodology, we (i) quantify the influence of different companies and, by studying
market similarity across time, present new insights into market structure and market stability, and (ii)
we present a practical application, which provides information on the how a company is influenced by
different economic sectors, and how the sectors interact with each other. These examples demonstrate
the effectiveness of this methodology in uncovering information valuable for a range of individuals,
including not only investors and traders but also regulators and policy makers.We wish to thank ONR (Grant N00014-09-1-0380, Grant N00014-12-1-0548), DTRA (Grant HDTRA-1-10-1- 0014, Grant HDTRA-1-09-1-0035), NSF (Grant CMMI 1125290), the European MULTIPLEX (EU-FET project 317532), CONGAS (Grant FP7-ICT-2011-8-317672), FET Open Project FOC 255987 and FOC-INCO 297149, and LINC (no. 289447 funded by the ECs Marie-Curie ITN program) projects, DFG, the Next Generation Infrastructure (Bsik), Bi-national US-Israel Science Foundation (BSF) and the Israel Science Foundation for financial support. (N00014-09-1-0380 - ONR; N00014-12-1-0548 - ONR; HDTRA-1-10-1- 0014 - DTRA; HDTRA-1-09-1-0035 - DTRA; CMMI 1125290 - NSF; 317532 - European MULTIPLEX (EU-FET project); FP7-ICT-2011-8-317672 - CONGAS; FOC 255987 - FET Open Project; FOC-INCO 297149 - FET Open Project; 289447 - LINC project - (ECs Marie-Curie ITN program); DFG; Next Generation Infrastructure (Bsik); Bi-national US-Israel Science Foundation (BSF); Israel Science Foundation)Accepted manuscrip
Biofriendly and Regenerable Emotional Monitor from Interfacial Ultrathin 2D PDA/AuNPs Crosslinking Film
Biofriendly and Regenerable Emotional Monitor from Interfacial Ultrathin 2D PDA/AuNPs Cross-linking Film