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Controlling systemic risk - network structures that minimize it and node properties to calculate it

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Document pages: 11 pages

Abstract: Evaluation of systemic risk in networks of financial institutions in generalrequires information of inter-institution financial exposures. In the frameworkof Debt Rank algorithm, we introduce an approximate method of systemic riskevaluation which requires only node properties, such as total assets andliabilities, as inputs. We demonstrate that this approximation captures a largeportion of systemic risk measured by Debt Rank. Furthermore, using Monte Carlosimulations, we investigate network structures that can amplify systemic risk.Indeed, while no topology in general sense is { em a priori} more stable if themarket is liquid [1], a larger complexity is detrimental for the overallstability [2]. Here we find that the measure of scalar assortativity correlateswell with level of systemic risk. In particular, network structures with highsystemic risk are scalar assortative, meaning that risky banks are mostlyexposed to other risky banks. Network structures with low systemic risk arescalar disassortative, with interactions of risky banks with stable banks.

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