Elimination of systemic risk in financial networks by means of a systemic risk transaction tax. Issue 10 (2nd October 2016)

• Record Type: Journal Article
• Authors: Poledna, Sebastian; Thurner, Stefan
• Is Part Of: Quantitative finance. Volume 16:Issue 10(2016)
• Page Start: 1599
• Page End: 1613
• Publication Date: 2016-10-02
• Subjects: Systemic risk -- Resilience -- Agent-based modelling -- Self-organization -- Network optimization -- DebtRank -- Banking regulation -- Financial transactions taxes -- Sustainability; C63 -- D85 -- G01 -- G18 -- G21; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2016.1156146
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : Financial markets are exposed to systemic risk (SR), the risk that a major fraction of the system ceases to function, and collapses. It has recently become possible to quantify SR in terms of underlying financial networks where nodes represent financial institutions, and links capture the size and maturity of assets (loans), liabilities and other obligations, such as derivatives. We demonstrate that it is possible to quantify the share of SR that individual liabilities within a financial network contribute to the overall SR. We use empirical data of nationwide interbank liabilities to show that the marginal contribution to overall SR of liabilities for a given size varies by a factor of a thousand. We propose a tax on individual transactions that is proportional to their marginal contribution to overall SR. If a transaction does not increase SR, it is tax-free. With an agent-based model (ABM) (CRISIS macro-financial model), we demonstrate that the proposed 'Systemic Risk Tax' (SRT) leads to a self-organized restructuring of financial networks that are practically free of SR. The SRT can be seen as an insurance for the public against costs arising from cascading failure. ABM predictions are shown to be in remarkable agreement with the empirical data and can be used to understand the relation of credit risk and SR.