New Ways of Doing Business, New Tools...

CDS transactions on exchange to provide data to calculate risk

You can also use computational biology (via an algorithm similar to Google's Page Rank algorithm) to analyze the CDS relationships between companies to determine if the extinction of a company would cause the collapse of the financial ecosystem.


Stefano Allesina with the National Center for Ecological Analysis and Synthesis at the University of California, Santa Barbara, and Mercedes Pascual of the University of Michigan at Ann Arbor have come up with an algorithm to rank the importance of species based upon the extinctions they would cause if they became extinct themselves.


Putting CDS transactions on an exchange would allow the risk individual institutions posed to be calculated regularly.


"Imagine removing -- due to insolvency, lack of counterparty confidence, lack of shareholder confidence, etc. -- one of the nodes in the middle of the graph with lots of connections. What does that do to the detailed cancelations that reduce the notional value of $45 trillion to something more manageable? Suddenly, perfectly healthy nodes in the system have uncanceled liabilities or unhedged positions to deal with, and the net value of contracts skyrockets. This is why some entities are too connected to fail, as opposed to too BIG to fail."


Googling Food Webs: Can an Eigenvector Measure Species' Importance for Coextinctions?


Stefano Allesina1*, Mercedes Pascual2,3,4

1 National Center for Ecological Analysis and Synthesis, Santa Barbara, California, United States of America, 2 Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America, 3 Santa Fe Institute, Santa Fe, New Mexico, United States of America, 4 Howard Hughes Medical Institute




A major challenge in ecology is forecasting the effects of species' extinctions, a pressing problem given current human impacts on the planet. Consequences of species losses such as secondary extinctions are difficult to forecast because species are not isolated, but interact instead in a complex network of ecological relationships. Because of their mutual dependence, the loss of a single species can cascade in multiple coextinctions. Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions, providing the sequence of losses that results in the fastest collapse of the network. Moreover, we use the algorithm to bridge the gap between qualitative (who eats whom) and quantitative (at what rate) descriptions of food webs. We show that our simple algorithm finds the best possible solution for the problem of assigning importance from the perspective of secondary extinctions in all analyzed networks. Our approach relies on network structure, but applies regardless of the specific dynamical model of species' interactions, because it identifies the subset of coextinctions common to all possible models, those that will happen with certainty given the complete loss of prey of a given predator. Results show that previous measures of importance based on the concept of “hubs” or number of connections, as well as centrality measures, do not identify the most effective extinction sequence. The proposed algorithm provides a basis for further developments in the analysis of extinction risk in ecosystems.



Iterative ranking algorithms have been used in economics:


But the big surprise is Franceschet's discovery of an even earlier forerunner to PageRank in the work of the Harvard economist Wassily Leontief. In 1941, Leontief published a paper in which he divides a country's economy into sectors that both supply and receive resources from each other, although not in equal measure. One important question is: what is the value of each sector when they are so tightly integrated? Leontief's answer was to develop an iterative method of valuing each sector based on the importance of the sectors that supply it. Sound familiar? In 1973, Leontief was awarded the Nobel Prize in economics for this work.



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