DebtRank and the Network of Leverage

Battiston, Stefano; D’Errico, Marco; Gurciullo, Stefano

doi:10.3905/jai.2016.18.4.068

Cited by 12 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in (Zlatić et al, 2015;de Souza et al, 2016). As remarked in (Battiston et al, 2015(Battiston et al, , 2016aBardoscia et al, 2016) DebtRank moves towards extending the classical structural approach of Merton (1974) within a network of liabilities. This is precisely modeled in Barucca et al (2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rethinking Financial Contagion

2016

Self Cite

View full text Add to dashboard Cite

How, and to what extent, does an interconnected financial system endogenously amplify external shocks? This paper attempts to reconcile some apparently different views emerged after the 2008 crisis regarding the nature and the relevance of contagion in financial networks. We develop a common framework encompassing several network contagion models and show that, regardless of the shock distribution and the network topology, precise ordering relationships on the level of aggregate systemic losses hold among models. We argue that the extent of contagion crucially depends on the amount of information that each model assumes to be available to market players. Under no uncertainty about the network structure and values of external assets, the well-known Eisenberg and Noe (2001) model applies, which delivers the lowest level of contagion. This is due to a property of loss conservation: aggregate losses after contagion are equal to the losses incurred by those institutions initially hit by a shock. This property implies that many contagion analyses rule out by construction any loss amplification, treating de facto an interconnected system as a single aggregate entity, where losses are simply mutualised. Under higher levels of uncertainty, as captured for instance by the DebtRank model, losses become non-conservative and get compounded through the network. This has important policy implications: by reducing the levels of uncertainty in times of distress (e.g. by obtaining specific data on the network) policymakers would be able to move towards more conservative scenarios. Empirically, we compare the magnitude of contagion across models on a sample of the largest European banks during the years 2006-2016. In particular, we analyse contagion effects as a function of the size of the shock and the type of external assets shocked.

show abstract

Section: Introductionmentioning

confidence: 99%

“…• the acyclic DebtRank (aDR) model (Battiston et al, 2012b(Battiston et al, , 2015(Battiston et al, , 2016a,…”

Section: Introductionmentioning

confidence: 99%

Rethinking Financial Contagion

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…When βλ max < 1 an exogenous shock is progressively dampened by the dynamic, while when βλ max > 1 shocks are amplified 32 , 42 . We note that usually β is set to one in the literature on DebtRank 13 , 37 , while λ max depends on the specific pattern of interbank loans and is usually between zero and two. Here we consider only one snapshot of interbank exposures at a given time, so that λ max is fixed and equal to 1.38.…”

Section: Resultsmentioning

confidence: 99%

“…Second, the result about the limited effect of interbank exposure refers to the situation in which stress is propagated from a borrower to the lender only after the default of the borrower. However in practice, as pointed out in 34 , losses can occur even in absence of defaults because of credit quality devaluation – the situation in which lenders revalue their interbank assets because they perceive a decline in the ability of their counterparties to repay their debt – which DebtRank aims at capturing in a crude way 37 .…”

Section: Introductionmentioning

confidence: 99%

Reverse stress testing interbank networks

Grigat

Caccioli

2017

Sci Rep

View full text Add to dashboard Cite

We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock scenarios in stress testing. We consider in particular the case of distress propagation in an interbank market, and we study a network of 44 European banks, which we reconstruct using data collected from banks statements. By looking at the distribution across banks of the size of smallest exogenous shocks we rank banks in terms of their systemic importance, and we show the effectiveness of a policy with capital requirements based on this ranking. We also study the properties of smallest exogenous shocks as a function of the parameters that determine the endogenous amplification of shocks. We find that the size of smallest exogenous shocks reduces and that the distribution across banks becomes more localized as the system becomes more unstable.

show abstract

“…The study of the interbank network has attracted considerable attention, also for its practical importance. Two widely recognized algorithms to quantify losses due to financial contagion are the Furfine algorithm [12,32] and DebtRank [33][34][35][36][37][38][39]. The former is essentially a threshold model according to which a bank propagates distress to its creditors only after its default.…”

Section: Introductionmentioning

confidence: 99%

Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank

et al. 2016

View full text Add to dashboard Cite

We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.

show abstract

DebtRank and the Network of Leverage

Cited by 12 publications

References 17 publications

Rethinking Financial Contagion

Rethinking Financial Contagion

Reverse stress testing interbank networks

Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank

Contact Info

Product

Resources

About