The financial crisis has told us that markets of finance are some form of complex networks that no one can easily understand what has easily gone out of control. Systemic risk is not an isolated event but a particular situation of financial networks which is let on its own dynamics. It is a rising property, side effect of economic jargon which arises due to the complex interaction and economic interests of market players. Study of data and network science can help in shaping markets and institutions that are more concrete, stable and better suited for benefit of society at large. If big market players' economic influence is not considered then financial regulation will have no meaning.
A lot of part of financial system can be modelled as networks where financial institutions are the nodes and contracts are links. We have a directed and weighted graph here as links(e.g. Loan) can be associated with weight and direction. Network Structural properties(e.g. distribution of no. of links,community structures or modularity that measures organization in communities) can be described by statistics provided by Network theory and also it can be used to determine the importance of financial institutions based on certain criteria.
The paper "The power to control" describes the application of two different notions of centrality and controllability to concrete case studies. Paper provide the results of one of the first network analysis of the TARGET2 infrastructure for large payments in Europe. It is shown how the nodes that drive the system are not necessarily the hubs or those responsible for the largest volumes of transactions. In a nutshell, in a network, due to multiple chains of connections, it often happens that a small cog is able to move a large cog. These notions are useful to devise concrete ways in which regulators can try and control the well-functioning of certain markets.
However, one of the issues with financial networks is that often the structure is unknown due to confidentiality issues. Indeed, it is in the interest of individual institutions to keep their financial contracts undisclosed. This however prevents the regulator to assess precisely the systemic risk, which depends critically on the overall structure of the network. The error in the estimation is a sort of "social price of private confidentiality". The paper "Reconstructing a credit network" sketches some of the methods that have been recently developed in order to deal with this problem. It is possible to estimate the macroscopic characteristics of a network as well as its resilience starting from limited information on the existing links. It is also possible to estimate financial interdependence based on time series of certain market indices such as the spread of credit default swaps associated to a given institution. These methods will hopefully contribute to building more reliable Early Warning Systems that detect the building up of financial instabilities.
The bad news is that even if certain properties of network structures can be estimated from partial information or from market indices time series, a more fundamental issue lures at regulators from behind the scenes. As outlined in the paper “Complex derivatives”, there are many incentives at work for market players to engage in an intricate web of complex derivative contracts that, overall constitutes in itself a too big to fail entity that will always be rescued at with public money. Because derivative contracts essentially amplify gain and losses and because they can depend on the financial health of other agents in the network, the resulting system is highly non-linear and intrinsically unstable. We are not even yet able to model the dynamics of its components and certainly very far from being able to predict anything of its global dynamics. In a nutshell, one possible view here is that derivatives, although can be used to hedge risks, are actually many times used to take excessive risk at the expenses of society at large, thus raising a serious moral hazard issue. The challenge for regulators is really formidable here. Network science seems a precondition for trying and understanding the positive feedbacks that are at play in this complex system.
In this respect, the paper “Network opportunities” argues that the problem of the economic discipline so far has been precisely not to be able to deal with these positive feedback. For various reasons, both the econometric approach and the so-called Stochastic Dynamic General Equilibrium (SDGE) approach are essentially linear and unable to model the instabilities and regime shifts that financial markets display so often. It is clear that better science alone will not resolve economic crises, nor it will allow the precise prediction of the economic or financial future. Certainly, however it seems to provide genuinely new and promising tools to help regulators and economists to understand and mitigate systemic risk.
1. Nature Physics Journal, March 2013, Volume 9, 3 ppl119-197 : Focus on Complex Networks in Finance
2. Financial market as Complex Network - Dr. Simone Alfarano, Dr. Albrecht Irle, Dr. Thomas Lux, Dr. Friedrich Wagner