Sebastian Poledna

DebtRank-transparency: Controlling systemic risk in financial networks

Nodes in a financial network, such as banks, cannot assess the true risks associated with lending to other nodes in the network, unless they have full information on the riskiness of all other nodes. These risks can be estimated by using network metrics (as DebtRank) of the interbank liability network. With a simple agent based model we show that systemic risk in financial networks can be drastically reduced by increasing transparency, i.e. making the DebtRank of individual banks visible to others, and by imposing a rule, that reduces interbank borrowing from systemically risky nodes. This scheme does not reduce the efficiency of the financial network, but fosters a more homogeneous risk-distribution within the system in a self-organized critical way. The reduction of systemic risk is due to a massive reduction of cascading failures in the transparent system. A regulation-policy implementation of the proposed scheme is discussed.

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 (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.

Systemic Risk Management in Financial Networks with Credit Default Swaps

We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way that makes it more resilient to insolvency cascades. A regulator can use information about the topology of the interbank network to devise a systemic insurance surcharge that is added to the CDS spread. CDS contracts are thus effectively penalized according to how much they contribute to increasing systemic risk. CDS contracts that decrease systemic risk remain untaxed. We simulate this regulated CDS market using an agent-based model (CRISIS macro-financial model) and we demonstrate that it leads to an interbank system that is more resilient to insolvency cascades.

Modelling Dependent Risk With Copulas: An Application On Flooding Using Agent-Based Modelling

Geophysical hazards such as earthquakes, floods, tsunamis, volcanic eruptions and storms affect electricity transmission infrastructure by destroying its elements including grids, masts, interconnectors and other elements of the electricity transmission system. Extreme temperatures also have negative impacts on transmission capacities of electricity networks. This paper discusses historical evidence of impacts of geophysical hazards and how they lead to major blackouts, which took place during the last decades in France, in the Balkans region and in China. In 1999 France experienced the storms Lothar and Martin, which had the wind speed of 200 km/h and had severe impacts on electricity transmission infrastructure. For instance 0.5% of the total number of towers were affected, 10% of circuits and 180 substations were out of order (Eurelectric, 2006). In the middle of 2015 the heavy rainfall resulted in extensive flooding in the Balkan region, which affected Serbia, Bosnia and Herzegovina as well as Croatia. This was a real multi-risk event, which was followed by landslides, which damaged overhead lines and underground infrastructure as well as transformer stations, customer connections and metering equipment. This resulted in an interruption of power supply, which affected more than 250,000 customers. The Wenchuan earthquake, which happened in May 2008, was one of the most devastating earthquakes in the history of China for the last 60 years. It had the magnitude of 8.0 on the Richter scale and severely damaged regional infrastructure, including electricity systems, such as regional high voltage power transmission lines and distribution lines. The earthquake damaged three 500 kV electricity transmission lines, fifty six 220 kV transmission lines, one hundred and ten 35 kV lines and seven hundred ninety five 10kV lines. The lines tripped mainly because of the broken poles, fallen pylons and damages to transformers and circuit breakers (Eidinger, 2009). The destruction of electricity transmission infrastructure resulted in a blackout, which affected 2.5 million people. Based on the analysis of the reports about these blackouts, lessons learned as well as elicitations from stakeholders from different sectors such as transmission systems operations, NGOs, academia and international organizations, this paper provides recommendations on risk management and short and medium term response and recovery measures. References 1. Eidinger, J. (2009). Wenchuan earthquake impact to power systems. In Proceedings of the 2009 technical council on lifeline earthquake engineering (TCLEE) conference: lifeline earthquake engineering in a multihazard environment, Oakland, June. 2. Eurelectric, (2006). Impacts of Severe Storms on Electric Grids. Union of the Electricity Industry – Eurelectric.

Identifying Systemically Important Companies by Using the Credit Network of an Entire Nation

The notions of systemic importance and systemic risk of financial institutions are closely related to the topology of financial liability networks. In this work, we reconstruct and analyze the financial liability network of an entire economy using data of 50,159 firms and banks. Our analysis contains 80.2% of the total liabilities of firms towards banks and all interbank liabilities in the Austrian banking system. The combination of firm-bank networks and interbank networks allows us to extend the concept of systemic risk to the real economy. In particular, the systemic importance of individual companies can be assessed, and for the first time, the financial ties between the financial and the real economy become explicitly visible. We find that firms contribute to systemic risk in similar ways as banks do. We identify a set of mid-sized companies that carry substantial systemic risk. Their default would affect up to 40% of the Austrian financial market. We find that all firms together create more systemic risk than the entire financial sector. In 2008, the total systemic risk of the Austrian interbank network amounted to only 29% of the total systemic risk of the entire financial network consisting of firms and banks. The work demonstrates that the notions of systemically important financial institutions (SIFIs) can be directly extended to firms.