Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
Statistical physics contributes to new models and metrics for the study of financial network structure, dynamics, stability and instability.
Consistent valuation of interbank claims within an interconnected financial system can be found with a recursive update of banks' equities.
Bipartite networks model the structures of ecological and economic real-world systems, enabling hypothesis testing and crisis forecasting.
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.
Targeted immunisation policies limit distress propagation and prevent system-wide crises in financial networks according to sandpile models.
Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.
A dynamical microscopic theory of instability for financial networks reformulates the DebtRank algorithm in terms of basic accounting principles.
The speed of a financial crisis outbreak sets the maximum delay before intervention by central authorities is no longer effective.
Time series data from networks of credit default swaps display no early warnings of financial crises without additional macroeconomic indicators.
Complex networks detect the driver institutions of an interbank market and ascertain that intervention policies should be time-scale dependent.
Analysis of web search queries about a given stock, from the seemingly uncoordinated activity of many users, can anticipate the trading peak.