Credit Contagion in Financial Markets: A Network-Based Approach

Steinbacher, Matjaz; Steinbacher, Mitja; Steinbacher, Matej

doi:10.2139/ssrn.2068716

Cited by 11 publications

(4 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dense clustering and mutual exposure identify the TITF institutions, where super spreaders dominate in terms of network centrality and connectivity. Studies focusing on the role of shocks on the overall stability of the financial system (Allen and Gale 2000;D'Errico et al 2009;Gabbi et al 2012;Steinbacher et al 2013) and policies for market regulation are well represented in literature (Gai et al 2011;Gai and Kapadia 2010;Halaj and Kok 2015).…”

Section: Systemic Riskmentioning

confidence: 99%

Complex Networks in Finance

D’Arcangelis

Rotundo

2016

Lecture Notes in Economics and Mathematical Systems

View full text Add to dashboard Cite

The present paper can be considered as divided in two parts: in the first one, we provide a review of the methods of complex networks that have been mainly used in the applications to the analysis of financial data. We focus on the following topics: the usage of the correlation matrix, systemic risk, integrated ownership and control, board of directors, interbank networks, and mutual funds holdings structure. The second part shows this last subject and provides new analyses. The main findings outline that there are substantial differences in geographical allocation among the different European fund managers. Five larger European countries dominate the market of mutual funds. The belonging of UK and Swiss optouts of the eurozone could be a probable explanation for our results on community detection, that give a snapshot of a sort of "geographical organization" of the core of mutual funds portfolios.

show abstract

Section: Systemic Riskmentioning

confidence: 99%

Complex Networks in Finance

D’Arcangelis

Rotundo

2016

Lecture Notes in Economics and Mathematical Systems

View full text Add to dashboard Cite

show abstract

“…A financial crisis may be a prototypical case of contagion, since banks tend to be highly connected with large parts of the economy, and their financial failure may create a deleveraging, impacting directly on the balance sheet of their borrowers. Contagion was analysed with a dynamical approach in [6,11,13,21,28,29] and in an Ising setting by [23], followed by [16] and [9].…”

Section: Related Literaturementioning

confidence: 99%

Model risk on credit risk

Molins

Vives

2016

RDA

View full text Add to dashboard Cite

Abstract. This paper develops the Jungle model in a credit portfolio framework. The Jungle model is able to model credit contagion, produce doubly-peaked probability distributions for the total default loss and endogenously generate quasi phase transitions, potentially leading to systemic credit events which happen unexpectedly and without an underlying single cause. We show the Jungle model provides the optimal probability distribution for credit losses, under some reasonable empirical constraints. The Dandelion model, a particular case of the Jungle model, is presented, motivated and exactly solved. The Dandelion model provides an explicit example of doubly-peaked probability distribution for the credit losses. The Diamond model, another instance of the Jungle model, experiences the so called quasi phase transitions; in particular, both the U.S. subprime and the European sovereign crises are shown to be potential examples of quasi phase transitions. We suggest how the Jungle model is able to explain a series of empirical stylized facts in credit portfolios, hard to reconcile by some standard credit portfolio models. We look at model risk in a credit risk framework under the Jungle model, especially in relation to systemic risks posed by doubly-peaked distributions and quasi phase transitions.

show abstract

“…The total loss is then distributed over creditors in proportion to the amount of their respective contributions. To clear the network following the default of one or more institutions, in this paper, we will use algorithm in [20]. The values of centrality measures of these networks are node degree centrality, betweenness centrality, and closeness centrality, denoted C D , C B and C C , respectively.…”

Section: An Overview Of Ramsi Modelmentioning

confidence: 99%

Vietnamese Bank Liquidity Risk Study Using the Risk Assessment Model of Systemic Institutions

Duong¹,

Pham‐Hi

Phan³

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Abstract. This paper presents a liquidity risk management model allows to assess the impact of stress scenarios on a banking system within a top-down approach. The impact of stress scenarios on a banking system includes: (i) individual bank reactions to the shock, (ii) the shock transmission across banks, through interbank networks and financial market channels and (iii) the recover rate, the proportion of the debt a creditor receives in an event of a default. The macro economic model is estimated and simulated quarterly and the data in balance sheet is yearly for the Vietnamese banking system. The results show a high vulnerability of the trading portfolios and interbank market.

show abstract

Credit Contagion in Financial Markets: A Network-Based Approach

Cited by 11 publications

References 72 publications

Complex Networks in Finance

Complex Networks in Finance

Model risk on credit risk

Vietnamese Bank Liquidity Risk Study Using the Risk Assessment Model of Systemic Institutions

Contact Info

Product

Resources

About