Out of Sight, Out of Mind? On the Risk of Sub-Custodian Structures

Droll, Thomas; Podlich, Natalia; Wedow, Michael

doi:10.2139/ssrn.2797055

Cited by 2 publications

(1 citation statement)

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“…The systemic importance values based on the ∆-CoES for both custodian banks are among the highest while for the ∆-CoVaR based values are among the lowest. The ∆-CoES based values more accurately reflect the important role custodian banks play in times of distress as these banks hold accounts and securities for other financial institutions they are hard to replace (low substitutability [41,75]) and can have long sub-custody chains containing hidden risks [33]. However, since their market exposures tend to be fairly limited (State Street is an exception due it being one of the largest provider of ETFs) their resiliency tends to be on the high side.…”

Section: Unconditional Network Model On Gsibsmentioning

confidence: 99%

$Δ-$CoES

Aleksy¹

2022

Preprint

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In this paper the ∆-CoVaR method is extended in both the conditional and unconditional cases to be based on the Expected Shortfall (ES) using quantile regression with a more expansive distress definition. We find the resulting ∆-CoES measure to be complementary to the ∆-CoVaR and to be more effective than the ∆-CoVaR in measuring short-term changes in systemic risk and in identifying heterogeneity in the systemic risk contributions of financial institutions and linkages between institutions due to its lower robustness. For regulators, risk managers and market participants these properties are interesting from an economic standpoint when they require the increased sensitivity and heterogeneity of the ∆-CoES to set short-term capital requirements/risk limits, find problematic financial linkages, problematic financial institutions or have some kind of early warning system for the emergence of systemic risk. Lastly, the ∆-CoES is straightforward to estimate and would fit within recent regulatory frameworks such as the FRTB. To show the statistical advantages and properties empirically, the ∆-CoVaR and ∆-CoES methods are used on a large sample (from 31-12-1970 to 31-12-2020 1564 firms) of daily equity data from US financial institutions both in a system and network fashion. On a sample of 9 US-based GSIBS we also show the properties and the utility of the ∆-CoES when it comes to identifying problematic financial links.

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Section: Unconditional Network Model On Gsibsmentioning

confidence: 99%