The recent economic crisis has raised a wide awareness that the financial system should be considered as a complex network with financial institutions and financial dependencies respectively as nodes and links between these nodes. Systemic risk is defined as the risk of default of a large portion of financial exposures among institution in the network. Indeed, the structure of this network is an important element to measure systemic risk and there is no widely accepted methodology to determine the systemically important nodes in a large financial network. In this research, we introduce a metric for systemic risk measurement with taking into account both common idiosyncratic shocks as well as contagion through counterparty exposures. Our focus is on application of eigenvalue problems, as a robust approach to the ranking techniques, to measure systemic risk. Recently, the efficient algorithm has been developed for robust eigenvector problem to reduce to a nonsmooth convex optimization problem. We applied this technique and studied the performance and convergence behavior of the algorithm with different structure of the financial network  .
 In this research, we introduce a robust metric to identify Systemically Important Financial Institution (SIFI) in a financial network by taking into account both common idiosyncratic shocks and contagion through counterparty exposures. We develop an efficient algorithm to rank financial institutions by formulating a fixed point problem and reducing it to a non-smooth convex optimization problem. We then study the underlying distribution of the proposed metric and analyze the performance of the algorithm by using different financial network structures. Overall, our findings suggest that the level of interconnection and position of institutions in the financial network are important elements to measure systemic risk and identify SIFIs. Results show that increasing the levels of out- and in-degree connections of an institution can have a diverse impact on its systemic ranking. Additionally, on the empirical side, we investigate the factors which lead to the identification of Global Systemic Important Banks (G-SIB) by using a panel dataset of the largest banks in each country. Our empirical results supports the main findings of the theoretical model .
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The recent economic crisis has raised a wide awareness that the financial system should be considered as a complex network with financial institutions and financial dependencies respectively as nodes and links between these nodes. Systemic risk is defined as the risk of default of a large portion of financial exposures among institution in the network. Indeed, the structure of this network is an important element to measure systemic risk and there is no widely accepted methodology to determine the systemically important nodes in a large financial network.
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metric for systemic risk measurement with
robust metric to identify Systemically Important Financial Institution (SIFI) in a financial network by
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as well as
and
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Our focus is on application of eigenvalue problems, as a robust approach to the ranking techniques, to measure systemic risk. Recently, the efficient algorithm has been developed for robust eigenvector problem to reduce to a nonsmooth
We develop an efficient algorithm to rank financial institutions by formulating a fixed point problem and reducing it to a non-smooth
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applied this technique and studied the performance and convergence behavior
then study the underlying distribution of the proposed metric and analyze the performance
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with different structure of
by using different financial network structures. Overall, our findings suggest that the level of interconnection and position of institutions in
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are important elements to measure systemic risk and identify SIFIs. Results show that increasing the levels of out- and in-degree connections of an institution can have a diverse impact on its systemic ranking. Additionally, on the empirical side, we investigate the factors which lead to the identification of Global Systemic Important Banks (G-SIB) by using a panel dataset of the largest banks in each country. Our empirical results supports the main findings of the theoretical model