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 unavailabledue 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 between 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 riskover the market .
Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets is strongly affected by the interconnections among financial institutions . Yet, while the aggregate balance sheets of these institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions , thus failing in reproducing bipartite networks of security holdings (e . g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows to reproduce the correct topology of the network. We test the method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed MECAPM both in reproducing the network topology and in estimating systemic risk .
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The spreading
Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification
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and the resulting systemic risk strongly depend on the detailed structure of financial interconnections
is strongly affected by the interconnections among financial institutions
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financial institutions have to disclose their aggregated balance sheet data, the
the aggregate balance sheets of these institutions are publicly disclosed,
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often unavailabledue 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
mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of
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cast for
designed for monopartite
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(such as the interbank market), whereas, the network of their investment portfolios (i.e.
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the stock market)has received much less attention
thus failing in reproducing bipartite networks of security holdings (e
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g., investment portfolios).
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develop an improved reconstruction method , based on statistical mechanics concepts and
propose a reconstruction method based on constrained entropy maximization,
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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
financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows to reproduce the correct topology of the network. We test the method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed MECAPM both in
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providing reliable estimates of systemic riskover the market
in estimating systemic risk