Enhanced capital-asset pricing model for bipartite financial networks reconstruction
Statistical mechanics concepts reconstruct connections between financial institutions and the stock market, despite limited data disclosure.
T. Squartini, G. Caldarelli, G. Cimini
The spreading of financial distress in capital markets and the resulting systemic risk strongly depend on the detailed structure of financial interconnections. Yet, while financial institutions have to disclose their aggregated balance sheet data, the information on single positions is often unavailable due to privacy issues. The resulting challenge is that of using the aggregate information to statistically reconstruct financial networks and correctly predict their higher-order properties. However, standard approaches generate unrealistically dense networks, which severely underestimate systemic risk. Moreover, reconstruction techniques are generally cast for networks of bilateral exposures be- tween financial institutions (such as the interbank market), whereas, the network of their investment portfolios (i.e., the stock market) has received much less attention. Here we develop an improved reconstruction method, based on statistical mechanics concepts and tailored for bipartite market networks. Technically, our approach consists in the preliminary estimation of connection probabilities by maximum-entropy inference driven by entities capitalizations and link density, followed by a density-corrected gravity model to assign position weights. Our method is successfully tested on NASDAQ, NYSE and AMEX filing data, by correctly reproducing the network topology and providing reliable estimates of systemic risk over the market.
More in Reconstructing credit networks
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.
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.
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.