Credit contagion and credit risk

Hatchett, J. P. L.; Kühn, Reimer

doi:10.1080/14697680802464162

Cited by 34 publications

(28 citation statements)

References 17 publications

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“…The setting discussed here includes avalanche or contagion effects only indirectly when calibrated to a market situation in the state of crisis. Direct modeling of contagion is provided in [20,23].…”

Section: Adjusting To Different Market Situationsmentioning

confidence: 99%

Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations

Mühlbacher

Guhr

2018

SSRN Journal

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We review recent progress in modeling credit risk for correlated assets. We start from the Merton model which default events and losses are derived from the asset values at maturity. To estimate the time development of the asset values, the stock prices are used whose correlations have a strong impact on the loss distribution, particularly on its tails. These correlations are non-stationary which also influences the tails. We account for the asset fluctuations by averaging over an ensemble of random matrices that models the truly existing set of measured correlation matrices. As a most welcome side effect, this approach drastically reduces the parameter dependence of the loss distribution, allowing us to obtain very explicit results which show quantitatively that the heavy tails prevail over diversification benefits even for small correlations. We calibrate our random matrix model with market data and show how it is capable of grasping different market situations. Furthermore, we present numerical simulations for concurrent portfolio risks, i.e., for the joint probability densities of losses for two portfolios. For the convenience of the reader, we give an introduction to the Wishart random matrix model. * andreas.muehlbacher@uni-due.de arXiv:1803.00261v1 [q-fin.RM]

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Section: Adjusting To Different Market Situationsmentioning

confidence: 99%

Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations

Mühlbacher

Guhr

2018

SSRN Journal

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“…Credit risk is the most important risk in the credit risk transfer (CRT) market, and one of the key issues is dealing with credit risk contagion [1][2][3][4][5][6][7][8][9]. Modeling credit risk contagion in the CRT network is an important yet challenging problem; credit risk modeling involves examining the role of counterparty risks [2,4,6,9].…”

Section: Introductionmentioning

confidence: 99%

“…If a key investor is in financial distress or default, then any investor who is economically influenced by this given investor will be affected, including the providers and purchasers of credit derivatives and the banks with the investor's credit line. The direct correlation between firms caused by credit contagion leads to further complications in modeling the overall risk level, either portfolio or economy wide [3,6,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Modeling credit risk contagion in the CRT network is an important yet challenging problem; credit risk modeling involves examining the role of counterparty risks [2,4,6,9]. If a key investor is in financial distress or default, then any investor who is economically influenced by this given investor will be affected, including the providers and purchasers of credit derivatives and the banks with the investor's credit line.…”

Section: Introductionmentioning

confidence: 99%

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Credit Risk Contagion in an Evolving Network Model Integrating Spillover Effects and Behavioral Interventions

2018

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We introduce an evolving network model of credit risk contagion in the credit risk transfer (CRT) market. The model considers the spillover effects of infected investors, behaviors of investors and regulators, emotional disturbance of investors, market noise, and CRT network structure on credit risk contagion. We use theoretical analysis and numerical simulation to describe the influence and active mechanism of the same spillover effects in the CRT market. We also assess the reciprocal effects of market noises, risk preference of investors, and supervisor strength of financial market regulators on credit risk contagion. This model contributes to the explicit investigation of the connection between the factors of market behavior and network structure. It also provides a theoretical framework for considering credit risk contagion in an evolving network context, which is greatly relevant for credit risk management.

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“…Such a direct dependency of defaults is referred to as credit contagion. Both mechanisms underlying clustering of defaults have received considerable attention in the credit risk literature for more than a decade; see (Davis, Lo, 2001) and , (Rogge, Schonbucher, 2003), , (Neu,Kuhn, 2004), (Hatchett, K• uhn, 2006(Hatchett, K• uhn, , 2009, (Eglo , Leippold, Vanini, 2007). Evidence suggests that the dependence on common factors can by itself not explain observed levels of correlation, and that credit contagion, possibly in conjunction with the e ect of further unobserved macro-economic covariates | so-called frailty | is important to explain the data .…”

Section: Contagionmentioning

confidence: 99%

Three essays on liquidity and contagion

Toto¹

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Credit contagion and credit risk

Cited by 34 publications

References 17 publications

Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations

Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations

Credit Risk Contagion in an Evolving Network Model Integrating Spillover Effects and Behavioral Interventions

Three essays on liquidity and contagion

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