Conditional graphical models for systemic risk estimation
•The paper provides a stochastic framework for financial network models.•It is based on a conditional graphical Gaussian model.•Systemic risk is decomposed into country risk plus bank specific risk.•It is the first paper that considers more data sources in systemic risks estimation. Financial networ...
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| Published in | Expert systems with applications Vol. 43; pp. 165 - 174 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2015.08.047 |
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| Abstract | •The paper provides a stochastic framework for financial network models.•It is based on a conditional graphical Gaussian model.•Systemic risk is decomposed into country risk plus bank specific risk.•It is the first paper that considers more data sources in systemic risks estimation.
Financial network models are a useful tool to model interconnectedness and systemic risks in banking and finance. Recently, graphical Gaussian models have been shown to improve the estimation of network models and, consequently, the interpretation of systemic risks.
This paper provides a novel graphical Gaussian model to estimate systemic risks. The model is characterised by two main innovations, with respect to the recent literature: it estimates risks considering jointly market data and balance sheet data, in an integrated perspective; it decomposes the conditional dependencies between financial institutions into correlations between countries and correlations between institutions, within countries.
The model has been applied to study systemic risks among the largest European banks, with the aim of identifying central institutions, more subject to contagion or, conversely, whose failure could result in further distress or breakdowns in the whole system. The results show that, in the transmission of systemic risk, there is a strong country effect, that reflects the weakness or the strength of the underlying economies. Besides the country effect, the most central banks are those larger in size. |
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| AbstractList | •The paper provides a stochastic framework for financial network models.•It is based on a conditional graphical Gaussian model.•Systemic risk is decomposed into country risk plus bank specific risk.•It is the first paper that considers more data sources in systemic risks estimation.
Financial network models are a useful tool to model interconnectedness and systemic risks in banking and finance. Recently, graphical Gaussian models have been shown to improve the estimation of network models and, consequently, the interpretation of systemic risks.
This paper provides a novel graphical Gaussian model to estimate systemic risks. The model is characterised by two main innovations, with respect to the recent literature: it estimates risks considering jointly market data and balance sheet data, in an integrated perspective; it decomposes the conditional dependencies between financial institutions into correlations between countries and correlations between institutions, within countries.
The model has been applied to study systemic risks among the largest European banks, with the aim of identifying central institutions, more subject to contagion or, conversely, whose failure could result in further distress or breakdowns in the whole system. The results show that, in the transmission of systemic risk, there is a strong country effect, that reflects the weakness or the strength of the underlying economies. Besides the country effect, the most central banks are those larger in size. Financial network models are a useful tool to model interconnectedness and systemic risks in banking and finance. Recently, graphical Gaussian models have been shown to improve the estimation of network models and, consequently, the interpretation of systemic risks. This paper provides a novel graphical Gaussian model to estimate systemic risks. The model is characterised by two main innovations, with respect to the recent literature: it estimates risks considering jointly market data and balance sheet data, in an integrated perspective; it decomposes the conditional dependencies between financial institutions into correlations between countries and correlations between institutions, within countries. The model has been applied to study systemic risks among the largest European banks, with the aim of identifying central institutions, more subject to contagion or, conversely, whose failure could result in further distress or breakdowns in the whole system. The results show that, in the transmission of systemic risk, there is a strong country effect, that reflects the weakness or the strength of the underlying economies. Besides the country effect, the most central banks are those larger in size. |
| Author | Giudici, Paolo Cerchiello, Paola |
| Author_xml | – sequence: 1 givenname: Paola surname: Cerchiello fullname: Cerchiello, Paola email: paola.cerchiello@unipv.it – sequence: 2 givenname: Paolo surname: Giudici fullname: Giudici, Paolo email: giudici@unipv.it |
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| Cites_doi | 10.1016/j.jbankfin.2007.03.011 10.2139/ssrn.2050029 10.2139/ssrn.2249909 10.1016/j.jfs.2007.12.004 10.1111/j.1540-6261.1975.tb03158.x 10.2139/ssrn.2577961 10.1016/j.jbankfin.2011.10.006 10.1016/j.jbankfin.2013.11.041 10.1111/j.2517-6161.1990.tb01771.x 10.1214/aos/1176347003 10.1007/s10693-010-0097-0 10.1016/j.ijforecast.2013.09.004 10.1111/j.2517-6161.1990.tb01770.x 10.1016/j.jfineco.2011.12.010 10.1023/A:1007954718966 10.1214/aos/1176349260 10.1016/j.jeconom.2011.02.003 10.1287/mnsc.38.7.926 10.1093/biomet/85.4.960 |
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| Snippet | •The paper provides a stochastic framework for financial network models.•It is based on a conditional graphical Gaussian model.•Systemic risk is decomposed... Financial network models are a useful tool to model interconnectedness and systemic risks in banking and finance. Recently, graphical Gaussian models have been... |
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| SubjectTerms | Banking Banks Conditional independence Correlation Estimates Expert systems Financial risk management Gaussian Network models Networks Risk |
| Title | Conditional graphical models for systemic risk estimation |
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