Physics of financial networks
The field of Financial Networks is a paramount example of the novel applications of Statistical Physics that have made possible by the present data revolution. As the total value of the global financial market has vastly outgrown the value of the real economy, financial institutions on this planet have created a web of interactions whose size and topology calls for quantitative analysis by means of Complex Networks. Financial Networks are not only a playground for the use of basic tools of statistical physics as ensemble representation and entropy maximization; rather, their particular dynamics and evolution triggered theoretical advancements as the definition of DebtRank to measure the impact and diffusion of shocks in the whole systems. In this review, we present the state of the art in this field, starting from the different definitions of financial networks (based either on loans, on assets ownership, on contracts involving several parties – such as credit default swaps, to multiplex representation when firms are introduced in the game and a link with the real economy is drawn) and then discussing the various dynamics of financial contagion as well as applications in financial network inference and validation. We believe that this analysis is particularly timely since financial stability as well as recent innovations in climate finance, once properly analyzed and understood in terms of complex network theory, can play a pivotal role in the transformation of our society towards a more sustainable world.
More in Reconstructing credit networks
Consistent valuation of interbank claims within an interconnected financial system can be found with a recursive update of banks' equities.
New mathematical tools can help infer financial networks from partial data to understand the propagation of distress through the network.
Network-based metrics to assess systemic risk and the importance of financial institutions can help tame the financial derivatives market.
Time series data from networks of credit default swaps display no early warnings of financial crises without additional macroeconomic indicators.
Statistical mechanics concepts reconstruct connections between financial institutions and the stock market, despite limited data disclosure.
Processes believed to stabilize financial markets can drive them towards instability by creating cyclical structures that amplify distress.
The likelihood of stock prices bouncing on specific values increases due to memory effects in the time series data of the price dynamics.