The absence of evidence and the evidence of absence: an algorithmic approach for identifying operational outages in TARGET2

Glowka, Marc; Paulick, Jan; Schultze, Inga

doi:10.21314/jfmi.2018.089

Cited by 8 publications

(9 citation statements)

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“…Section 4.2 describes the intra week and intraday patterns of the outages. Unlike Glowka et al (2018), we have a list of outage events reported by the participants.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…To detect operational outages, we build on the approach by Glowka et al (2018) and Klee (2010), who have developed an algorithm for the European RTGS, TARGET2, and the American RTGS, Fedwire, respectively. Glowka et al (2018) looks in contrast to Klee (2010) at periods of unusual low activity. They argue that, in the event that a participant is not able to send in payment instructions via the standard channels, there may be back-up functionalities that allow at least a few (very urgent) payments to be made.…”

Section: Introductionmentioning

confidence: 99%

“…This could be the case if the activity during the outage period exceeds the 1 percentile threshold used. Unlike Glowka et al (2018), we have a data base containing the participants' reported outages over a two year period, which we used to test our algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Is There Anybody Out There? Detecting Operational Outages from LVTS Transaction Data

Arjania

Heijmans

2020

SSRN Journal

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“…Section 4.2 describes the intra week and intraday patterns of the outages. Unlike Glowka et al (2018), we have a list of outage events reported by the participants.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Is There Anybody Out There? Detecting Operational Outages from LVTS Transaction Data

Arjania

Heijmans

2020

SSRN Journal

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“…Such disrupted participants can be detected using an algorithm (Klee 2010),(Glowka, Paulick, and Schultze 2018). and(Arjani and Heijmans, 2020) have developed algorithms to detect potential operational problems of participants in the United States, the Eurosystem and the Canadian large value payment system, respectively.…”

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confidence: 99%

Measuring the Impact of a Failing Participant in Payment Systems

Heijmans¹,

Wendt²

2020

IMF Working Papers

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Banks and financial market infrastructures (FMIs) that are not able to fulfill their payment obligations can be a source of financial instability. This paper develops a composite risk indicator to evaluate the criticality of participants in a large value payment system network, combining liquidity risk and interconnections in one approach, and applying this to the TARGET2 payment system. Findings suggest that the most critical participants in TARGET2 are other payment systems, because of the size of underlying payment flows. Some banks may be critical, but this is mainly due to their interconnectedness with other TARGET2 participants. Central counterparties and central securities depositories are less critical. These findings can be used in financial stability analysis, and feed into central bank policies on payment system access, oversight, and crisis management.

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Monitoring Liquidity Management of Banks With Recurrent Neural Networks

2020

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Monitoring liquidity management of banks is one of the prime tasks of central banks. A bank that manages its liquidity inadequately can severely harm its liquidity position and potentially threaten the stability of the entire financial system. Central banks try to anticipate these risks by carefully monitoring the liquidity management of banks in large-value payment systems (LVPSs). Typically, they do this based on statistical methods in which various risk indicators related to the liquidity usage of banks are calculated from the transaction log of an LVPS. These indicators need to be manually analyzed by payment experts to find irregularities that could signal potential risks. Although statistical methods provide much insight into the liquidity management of banks, they do not scale well to the large number of banks that are subject to risk monitoring and the high velocity by which payments are nowadays settled. In this paper, we investigate whether the liquidity management of banks can be monitored more efficiently by anomaly detection. We construct different probabilistic classifiers that classify delta sequences of banks by the corresponding bank. A delta sequence captures the change in the liquidity position of a bank in an LVPS throughout a given day. Accordingly, anomalies in the intraday liquidity usage of banks are detected by determining whether the classifiers misclassify recent delta sequences that were not used to train the classifiers. Our results show that recurrent neural networks are well suited to perform this classification task and detect many irregularities in payment behavior that are interesting for the supervisors and operators of an LVPS.

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The absence of evidence and the evidence of absence: an algorithmic approach for identifying operational outages in TARGET2

Cited by 8 publications

References 0 publications

Is There Anybody Out There? Detecting Operational Outages from LVTS Transaction Data

Is There Anybody Out There? Detecting Operational Outages from LVTS Transaction Data

Measuring the Impact of a Failing Participant in Payment Systems

Monitoring Liquidity Management of Banks With Recurrent Neural Networks

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