Systemic Risk in Financial Networks: A Survey

Jackson, Matthew O.; Pernoud, Agathe

doi:10.1146/annurev-economics-083120-111540

Cited by 97 publications

(31 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rich and in-depth investigations into these events have demonstrated that the risks not only influence each individual entity in the system but also spread among the entities and evolve into system-wide crises. In other words, the entities in a financial market are interdependent on each other, forming a complex networked system that enables the contagion of risks through the interdependencies [4][5][6]. Financial systems are thus normally modeled as networks, such as the networks of financial institutions [7,8], the network of stock indices [9], and the network of commodity futures [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the connectedness of commodity future markets via the cross-correlation network

Hou

Pan²

2022

Front. Phys.

View full text Add to dashboard Cite

Financial markets are widely believed to be complex systems where interdependencies exist among individual entities in the system enabling the risk spillover effect. The detrended cross-correlation analysis (DCCA) has found wide applications in examining the comovement of fluctuations among financial time series. However, to what extent can such cross-correlation represent the spillover effect is still unknown. This article constructs the DCCA network of commodity future markets and explores its proximity to the volatility spillover network. Results show a moderate agreement between the two networks. Centrality measures applied to the DCCA networks are able to identify key commodity futures that are transmitting or receiving risk spillovers. The evolution of the DCCA network reveals a significant change in the network structure during the COVID-19 pandemic in comparison to that of the pre- and post-pandemic periods. The pandemic made the commodity future markets more interconnected leading to a shorter diameter for the network. The intensified connections happen mostly between commodities from different categories. Accordingly, cross-category risk spillovers are more likely to happen during the pandemic. The analysis enriches the applications of the DCCA approach and provides useful insights into understanding the risk dynamics in commodity future markets.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating the connectedness of commodity future markets via the cross-correlation network

Hou

Pan²

2022

Front. Phys.

View full text Add to dashboard Cite

show abstract

“…An agent's initial endowment includes all the agent's tangible and intangible assets but excludes the claims and liabilities the agent has towards the other agents. For outstanding surveys of this literature, we refer the reader to Glasserman and Young (2016) and Jackson and Pernoud (2021).…”

Section: Introductionmentioning

confidence: 99%

Centralized Clearing Mechanisms in Financial Networks: A Programming Approach

Csóka

Herings

2022

SSRN Journal

View full text Add to dashboard Cite

“…Indeed while these links allows banks to cope with liquidity fluctuations and transfer risk, they can also become channels through which distress can spread, turning an idiosyncratic shock into a systemic one. Solvency contagion played a major role in the Global Financial Crisis of 2007/08 [25] and thus received large attention from the literature (we remand the reader to recent reviews [26][27][28]). A general dynamical model for solvency contagion is represented by the DebtRank algorithm [29][30][31], which describes the following situation.…”

Section: Introductionmentioning

confidence: 99%

Dimensional Reduction of Solvency Contagion Dynamics on Financial Networks

Ricciardi¹,

Montagna²,

Caldarelli³

et al. 2022

Preprint

View full text Add to dashboard Cite

Modelling systems with networks has been a powerful approach to tame the complexity of several phenomena. Unfortunately, such an approach is often made difficult by the large number of variables to take into consideration. Methods of dimensional reduction are useful tools to rescale a complex dynamical network down to a low-dimensional effective system and thus to capture the global features of the dynamics. Here we study the application of the degree-weighted and spectral reduction methods to an important class of dynamical processes on networks: the propagation of credit shocks within an interbank network, modelled according to the DebtRank algorithm. In particular we introduce an effective version of the dynamics, characterised by functions with continuous derivatives that can be handled by the dimensional reduction. We test the reduction methods against the full dynamical system in different interbank market settings: homogeneous and heterogeneous networks generated from state-of-the-art reconstruction methods as well as networks derived from empirical e-MID data. Our results indicate that, for proper choices of the bank default probability, reduction methods are able to provide reliable estimates of systemic risk in the market, with the spectral reduction better handling heterogeneous networks. Finally we provide new physical insights on the nature and working principles of dimensional reduction methods.

show abstract

Systemic Risk in Financial Networks: A Survey

Cited by 97 publications

References 141 publications

Evaluating the connectedness of commodity future markets via the cross-correlation network

Evaluating the connectedness of commodity future markets via the cross-correlation network

Centralized Clearing Mechanisms in Financial Networks: A Programming Approach

Dimensional Reduction of Solvency Contagion Dynamics on Financial Networks

Contact Info

Product

Resources

About