Network tail risk estimation in the European banking system

Torri, Gabriele; Giacometti, Rosella; Tichý, Tomáš

doi:10.1016/j.jedc.2021.104125

Cited by 21 publications

(10 citation statements)

References 38 publications

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“…For all , i ≠ j , we have found that the (Δ)CoVaR of targeted entity j ’s risk depends only on the conditioning entity i ’s risk and takes no account of its dependence on the risks of all the remaining entities in the financial system. By including the conditioning events of all the entities, except for targeted entity j , we follow Torri et al [ 40 ] to formulate a multivariate extension of (Δ)CoVaR, namely, (Δ)MCoVaR. Specifically, we define the MCoVaR as the VaR of targeted entity j ’s risk under the condition that entity i experiences a systemic event and that the remaining entities are jointly in their median state.…”

Section: Methodsmentioning

confidence: 99%

“…Adrian and Brunnermeier [ 35 ] argued that ΔCoVaR can identify the existence of each edge. Meanwhile, Torri et al [ 40 ] took ΔMCoVaR into consideration as an alternative to ΔCoVaR. Specifically, an edge ( i , j ) directed from node i to node j exists if is positive (for the ΔCoVaR-based network) or if is positive (for the ΔMCoVaR-based network).…”

Section: Methodsmentioning

confidence: 99%

“…This possibility inspired Cao [ 38 ] and Bernardi and Petrella [ 39 ] to extend (Δ)CoVaR to become (Δ)MCoVaR, where the “M” stands for multiple or multivariate. In particular, Torri et al [ 40 ] proposed the MCoVaR of targeted entity j ’s risk, given that entity i experiences a systemic event and, at the same time, other entities (except for entities i and j ) are in a normal condition. Using the corresponding ΔMCoVaR, they quantified the contribution of entity i to entity j by simultaneously considering the normal conditions of the remaining entities.…”

Section: Introductionmentioning

confidence: 99%

“…This framework is contrary to ΔCoVaR, which takes no account of these simultaneous conditions. The superiority of ΔMCoVaR became the basis for Torri et al [ 40 ] to build a more representative conditional tail risk network.…”

Section: Introductionmentioning

confidence: 99%

“…3) We also consider formulating several measures of the topological structure by accounting for the edge weights we determine using the modified Δ(M)CoVaR. Our conditional tail risk network is similar to that of Torri et al [ 40 ] and Chen et al [ 55 ]. They, however, relied on a (regression) model under the normality or Student’s t distributional assumption, with an application to the European banking system or the Chinese financial system.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets

et al. 2022

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In a financial system, entities (e.g., companies or markets) face systemic risk that could lead to financial instability. To prevent this impact, we require quantitative systemic risk management we can carry out using conditional value-at-risk (CoVaR) and a network model. The former measures any targeted entity’s tail risk conditional on another entity being financially distressed; the latter represents the financial system through a set of nodes and a set of edges. In this study, we modify CoVaR along with its multivariate extension (MCoVaR) considering the joint conditioning events of multiple entities. We accomplish this by first employing a multivariate Johnson’s SU risk model to capture the asymmetry and leptokurticity of the entities’ asset returns. We then adopt the Cornish–Fisher expansion to account for the analytic higher-order conditional moments in modifying (M)CoVaR. In addition, we attempt to construct a conditional tail risk network. We identify its edges using a corresponding Delta (M)CoVaR reflecting the systemic risk contribution and further compute the strength and clustering coefficient of its nodes. When applying the financial system to global foreign exchange (forex) markets before and during COVID-19, we revealed that the resulting expanded (M)CoVaR forecast exhibited a better conditional coverage performance than its unexpanded version. Its superior performance appeared to be more evident over the COVID-19 period. Furthermore, our network analysis shows that advanced and emerging forex markets generally play roles as net transmitters and net receivers of systemic risk, respectively. The former (respectively, the latter) also possessed a high tendency to cluster with their neighbors in the network during (respectively, before) COVID-19. Overall, the interconnectedness and clustering tendency of the examined global forex markets substantially increased as the pandemic progressed.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets

et al. 2022

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Risk spillover networks in financial markets: Evidence from emerging markets

Jin

Liu

2023

Manage Decis Econ

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This paper empirically explores dynamic risk impact of extreme volatility between stock and foreign exchange markets in emerging markets. Based on CoVaR analysis, we construct networks to investigate the nonlinear and dynamic evolution characteristics of risk contagion. The results show that stock and foreign exchange markets exhibit asymmetric tail dependence. All networks present small world network characteristics. The risk tolerance impact is more noticeable than risk overflow and stock markets are more sensitive to risk contagion. In the minimum spanning tree (MST), Korean stock market and foreign exchange markets of India and China are critical nodes in the minimum path of risk overflow.

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The network econometrics of financial concentration

García

Rambaud

2023

Rev Manag Sci

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Network tail risk estimation in the European banking system

Cited by 21 publications

References 38 publications

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets

Risk spillover networks in financial markets: Evidence from emerging markets

The network econometrics of financial concentration

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