What is the Minimal Systemic Risk in Financial Exposure Networks?

Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased. It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empirical ones have been studied by [pichler2018]. Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topology, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk management through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal effects on systemic risk [poledna2017]. Ways to actually incentivize a self-organized formation toward optimal network configurations were introduced in [thurner2013] and [poledna2016]. For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets.Comment: 25 page

Epistemic policy networks in the European Union’s CBRN risk mitigation policy

This paper offers insights into an innovative and currently flagship approach of the European Union (EU) to the mitigation of chemical, biological, radiological, and nuclear (CBRN) risks. Building on its long-time experience in the CBRN field, the EU has incorporated methods familiar to the students of international security governance: it is establishing regional networks of experts and expertise. CBRN Centers of Excellence, as they are officially called, aim to contribute to the security and safety culture in different parts of Africa, the Middle East, South East Asia, and South East Europe, in the broadly construed CBRN area. These regional networks represent a modern form of security cooperation, which can be conceptualized as an epistemic policy networks approach. It offers flexibility to the participating states, which have different incentives to get involved. At the same, however, the paper identifies potential limitations and challenges of epistemic policy networks in this form

Optimal monitoring and mitigation of systemic risk in lending networks

This thesis proposes optimal policies to manage systemic risk in financial networks. Given a one-period borrower-lender network in which all debts are due at the same time and have the same seniority, we address the problem of allocating a fixed amount of cash among the nodes to minimize the weighted sum of unpaid liabilities. Assuming all the loan amounts and cash flows are fixed and that there are no bankruptcy costs, we show that this problem is equivalent to a linear program. We develop a duality-based distributed algorithm to solve it which is useful for applications where it is desirable to avoid centralized data gathering and computation. Since some applications require forecasting and planning for a wide variety of different contingencies, we introduce a stochastic model for the institutions operating cash and consider the problem of minimizing the expectation of the weighted sum of unpaid liabilities. We show that this problem is a two-stage stochastic linear program and develop an online learning algorithm based on stochastic gradient descent to solve it. We consider a number of further extensions of our deterministic scenario by incorporating various additional features of real-world lending networks into our model. For example, we show that if the defaulting nodes do not pay anything, then the optimal cash injection allocation problem is a mixed-integer linear program. In addition, we develop and evaluate two heuristic algorithms to allocate the cash injection amount so as to minimize the number of nodes in default. Our results provide algorithmic tools to help financial institutions, banking supervisory authorities, regulatory agencies, and clearing houses in monitoring and mitigating systemic risk in financial networks

Illiquidity and all its Friends

The recent crisis was characterized by massive illiquidity. This paper reviews what we know and don't know about illiquidity and all its friends: market freezes, fire sales, contagion, and ultimately insolvencies and bailouts. It first explains why liquidity cannot easily be apprehended through a single statistics, and asks whether liquidity should be regulated given that a capital adequacy requirement is already in place. The paper then analyzes market breakdowns due to either adverse selection or shortages of financial muscle, and explains why such breakdowns are endogenous to balance sheet choices and to information acquisition. It then looks at what economics can contribute to the debate on systemic risk and its containment. Finally, the paper takes a macroeconomic perspective, discusses shortages of aggregate liquidity and analyses how market value accounting and capital adequacy should react to asset prices. It concludes with a topical form of liquidity provision, monetary bailouts and recapitalizations, and analyses optimal combinations thereof; it stresses the need for macro-prudential policies.Liquidity, Contagion, Bailouts, Regulation

Nonbanks and risk in retail payments

This paper documents the importance of nonbanks in retail payments in the United States and in 15 European countries and analyzes the implications of the importance and multiple roles played by nonbanks on retail payment risks. This paper also reviews the main regulatory safeguards in place, and concludes that there may be a need to reconsider some of them in view of the growing role of nonbanks and of the global reach of risks in the electronic era.

Too Interconnected To Fail: Financial Contagion and Systemic Risk in Network Model of CDS and Other Credit Enhancement Obligations of US Banks

Credit default swaps (CDS) which constitute up to 98% of credit derivatives have had a unique, endemic and pernicious role to play in the current financial crisis. However, there are few in depth empirical studies of the financial network interconnections among banks and between banks and nonbanks involved as CDS protection buyers and protection sellers. The ongoing problems related to technical insolvency of US commercial banks is not just confined to the so called legacy/toxic RMBS assets on balance sheets but also because of their credit risk exposures from SPVs (Special Purpose Vehicles) and the CDS markets. The dominance of a few big players in the chains of insurance and reinsurance for CDS credit risk mitigation for banks� assets has led to the idea of �too interconnected to fail� resulting, as in the case of AIG, of having to maintain the fiction of non-failure in order to avert a credit event that can bring down the CDS pyramid and the financial system. This paper also includes a brief discussion of the complex system Agent-based Computational Economics (ACE) approach to financial network modeling for systemic risk assessment. Quantitative analysis is confined to the empirical reconstruction of the US CDS network based on the FDIC Q4 2008 data in order to conduct a series of stress tests that investigate the consequences of the fact that top 5 US banks account for 92% of the US bank activity in the $34 tn global gross notional value of CDS for Q4 2008 (see, BIS and DTCC). The May-Wigner stability condition for networks is considered for the hub like dominance of a few financial entities in the US CDS structures to understand the lack of robustness. We provide a Systemic Risk Ratio and an implementation of concentration risk in CDS settlement for major US banks in terms of the loss of aggregate core capital. We also compare our stress test results with those provided by SCAP (Supervisory Capital Assessment Program). Finally, in the context of the Basel II credit risk transfer and synthetic securitization framework, there is little evidence that the CDS market predicated on a system of offsets to minimize final settlement can provide the credit risk mitigation sought by banks for reference assets in the case of a significant credit event. The large negative externalities that arise from a lack of robustness of the CDS financial network from the demise of a big CDS seller undermines the justification in Basel II that banks be permitted to reduce capital on assets that have CDS guarantees. We recommend that the Basel II provision for capital reduction on bank assets that have CDS cover should be discontinued.

Too Interconnected To Fail: Financial Contagion and Systemic Risk In Network Model of CDS and Other Credit Enhancement Obligations of US Banks

Credit default swaps (CDS) which constitute up to 98% of credit derivatives have had a unique, endemic and pernicious role to play in the current financial crisis. However, there are few in depth empirical studies of the financial network interconnections among banks and between banks and nonbanks involved as CDS protection buyers and protection sellers. The ongoing problems related to technical insolvency of US commercial banks is not just confined to the so called legacy/toxic RMBS assets on balance sheets but also because of their credit risk exposures from SPVs (Special Purpose Vehicles) and the CDS markets. The dominance of a few big players in the chains of insurance and reinsurance for CDS credit risk mitigation for banks’ assets has led to the idea of “too interconnected to fail” resulting, as in the case of AIG, of having to maintain the fiction of non-failure in order to avert a credit event that can bring down the CDS pyramid and the financial system. This paper also includes a brief discussion of the complex system Agent-based Computational Economics (ACE) approach to financial network modeling for systemic risk assessment. Quantitative analysis is confined to the empirical reconstruction of the US CDS network based on the FDIC Q4 2008 data in order to conduct a series of stress tests that investigate the consequences of the fact that top 5 US banks account for 92% of the US bank activity in the $34 tn global gross notional value of CDS for Q4 2008 (see, BIS and DTCC). The May-Wigner stability condition for networks is considered for the hub like dominance of a few financial entities in the US CDS structures to understand the lack of robustness. We provide a Systemic Risk Ratio and an implementation of concentration risk in CDS settlement for major US banks in terms of the loss of aggregate core capital. We also compare our stress test results with those provided by SCAP (Supervisory Capital Assessment Program). Finally, in the context of the Basel II credit risk transfer and synthetic securitization framework, there is little evidence that the CDS market predicated on a system of offsets to minimize final settlement can provide the credit risk mitigation sought by banks for reference assets in the case of a significant credit event. The large negative externalities that arise from a lack of robustness of the CDS financial network from the demise of a big CDS seller undermines the justification in Basel II that banks be permitted to reduce capital on assets that have CDS guarantees. We recommend that the Basel II provision for capital reduction on bank assets that have CDS cover should be discontinued.Credit Default Swaps; Financial Networks; Systemic Risk; Agent BasedCredit Default Swaps, Financial Networks, Systemic Risk, Agent Based Models, Complex Systems, Stress Testing