Elimination of systemic risk in financial networks by means of a systemic risk transaction tax

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 (CRISIS macro-financial model) we demonstrate that the proposed "Systemic Risk Tax" (SRT) leads to a self-organised 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.Comment: 18 pages, 7 figure

DebtRank-transparency: Controlling systemic risk in financial networks

Banks in the interbank network can not assess the true risks associated with lending to other banks in the network, unless they have full information on the riskiness of all the other banks. These risks can be estimated by using network metrics (for example DebtRank) of the interbank liability network which is available to Central Banks. With a simple agent based model we show that by increasing transparency by making the DebtRank of individual nodes (banks) visible to all nodes, and by imposing a simple incentive scheme, that reduces interbank borrowing from systemically risky nodes, the systemic risk in the financial network can be drastically reduced. This incentive scheme is an effective regulation mechanism, that does not reduce the efficiency of the financial network, but fosters a more homogeneous distribution of risk within the system in a self-organized critical way. We show that the reduction of systemic risk is to a large extent due to the massive reduction of cascading failures in the transparent system. An implementation of this minimal regulation scheme in real financial networks should be feasible from a technical point of view.Comment: 8 pages, 5 figure

Agent-based models in econophysics

Econophysics ist ein interdisziplinäres Forschungsgebiet, in dem Methoden aus der Physik, insbesondere aus der statistischen Mechanik, angewandt werden um Probleme im Finanzwesen und in der Ökonomie zu analysieren. Agenten-basierte Modelle (ABM), welche zur Untersuchung von komplexen Systemen verwendet werden können, sind eine Erweiterung des bekannten Ising-Modells, einem Modell des Ferromagnetismus in der statistischen Mechanik. Sie sind eine Klasse von Computermodellen zur Simulation von Aktionen und Interaktionen autonomer Agenten, die eingesetzt werden, um deren Auswirkungen auf das System als Ganzes zu studieren. Mit dem zunehmendem Gewinn an Popularität in den letzten zwei Jahrzehnten sind ABMs dabei, zu einem akzeptierten Instrument für die Analyse von wirtschaftlichen Problemen zu werden. Im ersten Teil dieser Arbeit wird ein Überblick über ABMs im Finanzwesen und in der Ökonomie gegeben, insbesondere werden verschiedene Designs künstlicher Märkte diskutiert. Im zweiten Teil wird ein ABM als Spielzeug-Modell des Finanzmarktes genutzt, um die Effizienz und die Gefahren von Bankenregulierungssystemen für das Kreditwesen zu testen. Die Simulationsergebnisse zeigen, dass die Basel-Regulierungsvorschriften gut in Situationen mit geringem leverage im Finanzsystem funktionieren. In Szenarien mit einem realistischeren leverage level haben sie aber destabilisierende Auswirkungen. Außerdem wurde eine "ideale Welt", wo alle durch leverage hervorgerufenen Risiken mit Optionen abgesichert werden, entwickelt. Selbst unter der Annahme, dass Optionsaussteller nie zahlungsunfähig werden, stellte sich heraus, dass durch die Einführung einer vollständigen Absicherung das System nicht systemisch sicherer wird.Econophysics is an interdisciplinary research area using methods from physics, in particular from statistical mechanics, in order to analyze problems in economics and finance. Agent-based models (ABM), which can be used to study complex systems, are an extension of the famous Ising model, a model of ferromagnetism in statistical mechanics. They are a class of computational models simulating actions and interactions of autonomous agents, which are employed to study their effects on the system as a whole. With ABMs gaining increasing popularity over the last two decades, they are about to become an accepted tool for the analysis of economic problems. In the first part of this work, an overview of ABMs in finance and economics is presented, in particular different designs of artificial markets are discussed. In the second part an ABM is used as a toy model of the financial market to test the efficiency and dangers of credit regulation schemes. The simulation results showed that Basle-type regulation works fine in situations of low leverage levels in the financial system, however they become destabilizing in scenarios with realistic leverage level. Furthermore an "ideal world", where all leverage introduced risk is hedged with options was designed. Even by assuming that option writers never default, it turned out that introducing the heavy requirement of complete hedging does not make the system systemically more secure

Recovery of the Austrian economy following the COVID-19 crisis can take up to three years, IIASA Policy Brief #26

Collaboration between researchers from IIASA, WU, WIFO, and the IHS provides scenarios of the medium-run economic effects of the lockdown in Austria using the IIASA macroeconomic simulation model. The analysis suggests that the return to the business-as-usual trend may take up to three years after a steep initial economic downturn due to the lockdown, and a gradual recovery thereafter

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007-2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor four higher now than before the financial crisis of 2007-2008. We find that systemic risk contributions of individual transactions can be up to a factor of thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data driven quantification of systemic risk on national scales that reveal its true levels.Comment: 15 pages, 6 figure