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Cyclical patterns in risk indicators based on financial market infrastructuretransaction data

1 De Nederlandsche Bank, Westeinde 1, 1017 ZN Amsterdam, Netherlands
2 Tilburg University, Warandelaan 2, 5037 AB Tilburg, Netherlands
3 Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA Rotterdam, Netherlands

Special Issues: Systemic Risk Measurement

This paper studies cyclical patterns in risk indicators based on TARGET2 transaction data.These indicators provide information on network properties, operational aspects and links to ancillarysystems. We compare the performance of two di erent ARIMA dummy models to the TBATS statespace model. The results show that the forecasts of the ARIMA dummy models perform better thanthe TBATS model. We also find that there is no clear di erence between the performances of the twoARIMA dummy models. The model with the fewest explanatory variables is therefore preferred.
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Keywords ARIMA; TBATS; time series; TARGET2; cyclical patterns

Citation: Monique Timmermans, Ronald Heijmans, Hennie Daniels. Cyclical patterns in risk indicators based on financial market infrastructuretransaction data. Quantitative Finance and Economics, 2018, 2(3): 615-636. doi: 10.3934/QFE.2018.3.615


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This article has been cited by

  • 1. Ronald Heijmans, Chen Zhou, Outlier Detection in TARGET2 Risk Indicators, SSRN Electronic Journal , 2019, 10.2139/ssrn.3332441
  • 2. Leonard Sabetti, Ronald Heijmans, Shallow or Deep? Detecting Anomalous Flows in the Canadian Automated Clearing and Settlement System using an Autoencoder, SSRN Electronic Journal , 2020, 10.2139/ssrn.3581595

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