Cyclical patterns in risk indicators based on financial market infrastructuretransaction data

Monique Timmermans; Ronald Heijmans; Hennie Daniels; Monique Timmermans; Ronald Heijmans; Hennie Daniels

doi:10.3934/QFE.2018.3.615

Quantitative Finance and Economics

2018, Volume 2, Issue 3: 615-636. doi: 10.3934/QFE.2018.3.615

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Research article Special Issues

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

Received: 22 March 2018 Accepted: 05 July 2018 Published: 03 August 2018
JEL Codes: E42, E50, E58, E59

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 ancillary systems. We compare the performance of two di erent ARIMA dummy models to the TBATS state space model. The results show that the forecasts of the ARIMA dummy models perform better than the TBATS model. We also find that there is no clear di erence between the performances of the two ARIMA dummy models. The model with the fewest explanatory variables is therefore preferred.
- 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[J]. Quantitative Finance and Economics, 2018, 2(3): 615-636. doi: 10.3934/QFE.2018.3.615

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Abstract

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 ancillary systems. We compare the performance of two di erent ARIMA dummy models to the TBATS state space model. The results show that the forecasts of the ARIMA dummy models perform better than the TBATS model. We also find that there is no clear di erence between the performances of the two ARIMA dummy models. The model with the fewest explanatory variables is therefore preferred.

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