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Complex Networks in Finance: Nature Physics Journal on Financial Complexity


Why Nature Physics has released an issue focusing on physicists and economists considering the state-of-the-art in the application of network science to finance?

The financial crisis has made us aware that financial markets are very complex networks that, in many cases, we do not really understand and that can easily go out of control.

This idea, which would have been shocking only 5 years ago, results from a number of precise reasons. That’s why Nature Physics has released an issuefocusing on physicists and economists considering the state-of-the-art in the application of network science to finance. For the first time, a group of network scientists, economists, and regulators teamed up to discuss for a wider audience the fundamental challenges posed by complex financial networks to the stability of our economies. The contributers include INET grantee Stefano Battiston INET advisory board member and Nobel laureate Joseph Stiglitz. The Focus consists of four interconnected papers that:

  • Offer some intuition of why financial networks can get so complex and unstable
  • Provide examples of the new network approaches that are currently pursued by regulators
  • Indicate research avenues that should to be explored in the future

The Focus represents an important outcome of the work carried out in the European project FOC that deals with alternative methods to model economic complexity and financial crises. Two of the papers also have been supported by the Institute of New Economic Thinking.

Overall, the arguments and the findings presented in the Focus could be roughly summarized as follows. Systemic risk is not a remote event but a typical situation of financial networks let on their own dynamics. Systemic risk is an emerging property, an externality in the economic jargon, that arises from the complex interaction of the private economic interests of market players. More data and more network science can help us shaping institutions and markets that are better suited for the good of society at large. However, financial regulation is of little effect if the economic influence of big market players is not seriously addressed.

Other authors include the mathematician and ecologist Lord Robert May, a representative of the Bank of England and former European Central Bank adviser (Marco Galbiati), a representative of the Deutsche Bundesbank (Co-Pierre Georg), and the the co-founder of the FOC project (Guido Caldarelli, who founded the project with Stefano Battiston). Additional authors include both economists and physicists.

More details.

In more detail, the four papers can be outlined as follows.

Many portions of the financial system can be thought of as networks in which financial institutions are the nodes and financial contracts such as loans or derivatives are the links. Links are in general directed and weighted as they can associated for instance with the value of the contract. Network science provides statistics to describe the overall network structure (i.e. the distribution of the number of links or the modularity that measures the organization in communities) but also measures to assess the importance of individual nodes according to certain criteria. The algorithm DebtRank, cited several times in the Focus represents a successful example of a method recently introduced to overcome the limitations of the state of the art. It includes network effects that were previously neglected in the propagation of distress and it is currently being tested by researchers at various central banks.

The paper “The power to control” explains how the two different notions of centrality and controllability can be applied to concrete case studies. In particular, the paper reports 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 the multiple chains if 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 is constitutes in itself a too-big-to-fail entity that will always be rescued at the 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 feedbacks. 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 is seems to provide genuinely new and promising tools to help regulators and economists to understand and mitigate systemic risk.