Outlier Detection in TARGET2 Risk Indicators

29 Pages Posted: 12 Feb 2019

See all articles by Ronald Heijmans

Ronald Heijmans

De Nederlandsche Bank

Chen Zhou

De Nederlandsche Bank; Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)

Date Written: February 7, 2019

Abstract

This paper studies the detection of outliers in risk indicators based on large value payment system transaction data. The ten risk indicators are daily time series measuring various risks in the large value payment system, such as operational risk, concentration risk and liquidity flows related to other financial market infrastructures. We use extreme value theory and local outlier factor methods to identify anomalous data points (outliers). In a univariate setup, the extreme value analysis quantifies the unusualness of each outlier. In a multivariate setup, the local outlier factor method identifies outliers by measuring the local deviation of a given data point with respect to its neighbours. We find that most detected outliers are at the beginning and near end of the calendar month when turnover is significantly larger than at other days. Our method can be used e.g. by overseers and financial stability experts who wish to look at many (risk) indicators in relation to each other.

Keywords: risk indicator, TARGET2, financial market infrastructure, extrem value theory (EVT), local outlier factor (LOF), anomaly

JEL Classification: E42, E50, E58, E59

Suggested Citation

Heijmans, Ronald and Zhou, Chen, Outlier Detection in TARGET2 Risk Indicators (February 7, 2019). De Nederlandsche Bank Working Paper No. 624, January 2019, Available at SSRN: https://ssrn.com/abstract=3332441 or http://dx.doi.org/10.2139/ssrn.3332441

Ronald Heijmans (Contact Author)

De Nederlandsche Bank ( email )

PO Box 98
1000 AB Amsterdam
Amsterdam, 1000 AB
Netherlands

Chen Zhou

De Nederlandsche Bank ( email )

PO Box 98
1000 AB Amsterdam
Amsterdam, 1000 AB
Netherlands

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

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