scholarly journals Market Structure, Counterparty Risk, and Systemic Risk

2011 ◽  
Author(s):  
Dale W. R. Rosenthal
Keyword(s):  
Market Structure ◽  
Systemic Risk ◽  
Counterparty Risk

CVA UNDER ALTERNATIVE SETTLEMENT CONVENTIONS AND WITH SYSTEMIC RISK

2013 ◽  
Vol 16 (07) ◽  
pp. 1350039 ◽  
Author(s):  
CYRIL DURAND ◽  
MAREK RUTKOWSKI
Keyword(s):  
Comparative Analysis ◽  
Systemic Risk ◽  
General Framework ◽  
Practical Importance ◽  
Numerical Results ◽  
Counterparty Risk ◽  
Market Environment ◽  
Financial Contracts

We propose a fairly general framework which allows one to perform Credit Value Adjustment (CVA) computations for a contract with bilateral counterparty risk in the presence of (a) systemic risk and (b) wrong-way or right-way risks. Our methodology focuses on the role of alternative settlement clauses, but it also aims to cover various features of margin agreements. We present a comparative analysis of numerical results that supports our initial conjecture that alternative specifications of settlement values have a nonnegligible impact on CVA computations for contracts with bilateral counterparty risk. Our conclusions emphasize the practical importance of more sophisticated models that are capable of fully reflecting the actual features of financial contracts, as well as the influence of the market environment.

scholarly journals Heterogeneous market structure and systemic risk: Evidence from dual banking systems

2017 ◽  
Vol 33 ◽  
pp. 96-119 ◽  
Author(s):  
Pejman Abedifar ◽  
Paolo Giudici ◽  
Shatha Qamhieh Hashem
Keyword(s):  
Market Structure ◽  
Systemic Risk ◽  
Banking Systems

scholarly journals Systemic Risk Modeling

2020 ◽  
Vol 20 (54) ◽  
Author(s):  
Raphael Espinoza ◽  
Miguel Segoviano ◽  
Ji Yan
Keyword(s):  
Theoretical Model ◽  
Empirical Model ◽  
Systemic Risk ◽  
Empirical Models ◽  
Equity Returns ◽  
Counterparty Risk ◽  
Risk Modeling ◽  
Cross Sectional ◽  
Fire Sales ◽  
Multivariate Density

We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.

scholarly journals Systemic Risk Contagion in Reconstructed Financial Credit Network within Banking and Firm Sectors on DebtRank Based Model

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yuetang (Peter) Bian ◽  
Yu Wang ◽  
Lu Xu
Keyword(s):  
Systemic Risk ◽  
Computational Simulation ◽  
Balance Sheet ◽  
Nonlinear Behavior ◽  
Influential Factors ◽  
Counterparty Risk ◽  
Negative Effects ◽  
Reconstructed Network ◽  
Negative Effect ◽  
Credit Network

This paper is dedicated to building a multilayer financial network within banking sectors and firm sectors (nonbanking) on the balance sheet of two types of agents and to assessing systemic risk contagion in the reconstructed network. Two propagation channels due to interbank credit and counterparty risk via banks’ loans to firms are comprehensively taken into account in systemic risk contagion assessment, which is based on the DebtRank model by analyzing the relative loss of each bank’s equity and the vulnerability of the network. The computational simulation on how systemic risk contagious process evolves has been conducted, where the possible influential factors of network structure, agent’s initial risk status, external shock ratio, liquidity flow rate, and different layers of the network are considered. The findings show that the reconstructed network is absolutely vulnerable under the assumed market circumstance without any bailouts and the risk contagion process shows nonlinear behavior. Specifically, when the average degree of the network and the external shock ratio increases, the risk contagion speed becomes relatively high and the resulting negative effects on the network are more intense. Besides, risks originating from the failed firms in bank-firm layer should place more negative effect on the financial system than that only happening in interbank market. Different liquidity rates in financial market could lead to obvious discrepancy of the risk contagion speed and the extent of asset loss. Additionally, the two layers of the network have diverse influences on risk contagious process resulting in totally different banks’ status in each layer.

Network effects, cascades and CCP interoperability

2014 ◽  
Vol 25 (09) ◽  
pp. 1450035
Author(s):  
Xiaobing Feng ◽  
Haibo Hu ◽  
Matthew Pritsker
Keyword(s):  
Systemic Risk ◽  
Risk Sharing ◽  
Network Effects ◽  
Threshold Value ◽  
Cascading Failures ◽  
Counterparty Risk ◽  
Financial Regulations ◽  
Logistic Mapping ◽  
The World ◽  
Near Term

To control counterparty risk, financial regulations such as the Dodd Frank Act are increasingly requiring standardized derivatives trades to be cleared by central counterparties (CCPs). It is anticipated that in the near-term future, CCPs across the world will be linked through interoperability agreements that facilitate risk-sharing but also serve as a conduit for transmitting shocks. This paper theoretically studies a network with CCPs that are linked through interoperability arrangements, and studies the properties of the network that contribute to cascading failures. The magnitude of the cascading is theoretically related to the strength of network linkages, the size of the network, the logistic mapping coefficient, a stochastic effect and CCP's defense lines. Simulations indicate that larger network effects increase systemic risk from cascading failures. The size of the network N raises the threshold value of shock sizes that are required to generate cascades. Hence, the larger the network, the more robust it will be.

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