How to Measure the Unsecured Money Market? The Eurosystem's Implementation and Validation Using TARGET2 Data

Arciero, Luca; Heijmans, Ronald; Heuver, Richard; Massarenti, Marco; Picillo, Cristina; Vacirca, Francesco

doi:10.2139/ssrn.2206337

Cited by 34 publications

(18 citation statements)

References 8 publications

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“…interbank loans) from interbank payments settled through TARGET2 for maturities ranging from one day (overnight) up to one year (see Arciero et al (2013)). This methodology relies on a refined version of the algorithm originally developed by Furfine (1999) to find loan-refund combinations from payment data.…”

Section: The Institutional Frameworkmentioning

confidence: 99%

“…In contrast with the validation exercise recently carried out by Armantier and Copeland (2012) on a "plain-vanilla" implementation of the Furfine algorithm done at the Federal Reserve Bank of New York, the performance of the Eurosystem's algorithm that we use has been found to be very encouraging. More specifically, Arciero et al (2013) report a very low Type 2 error of 0.92% for the best algorithm setup, of which only 0.26% represent wrong matches (see Arciero et al (2013) for more details on the sources used for and the results of the validation). In contrast to the Furfine implementation used for the Fedwire analyses, our data contains not only the settlement banks involved, but also the initial originator and final beneficiary of the transactions.…”

Section: The Institutional Frameworkmentioning

confidence: 99%

“…3 Interbank loans are identified using the algorithm originally developed by Furfine (1999) in the implementation of Arciero et al (2013). 4 Our data contains unsecured interbank transactions with maturity of up to one year.…”

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confidence: 99%

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A Network View on Interbank Market Freezes

Gabrieli

Georg

2014

SSRN Journal

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We study the liquidity allocation among European banks around the Lehman insolvency using a novel dataset of all interbank loans settled via the Eurosystem's payment system TARGET2. Following the Lehman insolvency, lenders in the overnight segment become sensitive to counterparty characteristics and banks start hoarding liquidity by shortening the maturity of their interbank lending. This aggregate change in liquidity reallocation is accompanied by a substantial structural change that can best be characterized as a shrinking of the interbank network. Such a change in the network structure is consequential: banks with higher centrality within the network have better access to liquidity and are able to charge larger intermediation spreads. Therefore, we show the existence of a sizeable interbank lending channel.

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Section: The Institutional Frameworkmentioning

confidence: 99%

Section: The Institutional Frameworkmentioning

confidence: 99%

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A Network View on Interbank Market Freezes

Gabrieli

Georg

2014

SSRN Journal

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“…commercial bank or insurance company), 3) to financial stability experts who have an interest in the well-being of the financial system as a whole and 4) monetary policy experts who are interested in the well-functioning of the money markets. Examples of how FMI transaction data has been used are Berndsen and Heijmans (2017) who develop risk indicators for the most important euro-denominated large-value payment system (TARGET2), Arciero et al (2016) who identify unsecured interbank money market loans from TARGET2 and Baek et al (2014) who define network indicators for monitoring intraday liquidity in the Korean large value payment system (BoKwire).…”

Section: Introductionmentioning

confidence: 99%

Cyclical Patterns in Risk Indicators Based on Financial Market Infrastructure Transaction Data

2017

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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractThis 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 different 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 difference between the performances of the two ARIMA dummy models. The model with the fewest explanatory variables is therefore preferred.Keywords: ARIMA, TBATS, Time Series, TARGET2, Cyclical Patterns. JEL classifications: E42, E50, E58, E59.* Timmermans, Heijmans and Daniels can be reached at m.t.h.timmermans@dnb.nl, ronald.heijmans@dnb.nl and H.A.M.Daniels@uvt.nl, respectively. Heijmans is a member of one of the user groups with access to TARGET2 data in accordance with Article 1(2) of Decision ECB

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“…Fedwire system; for details on the dataset and methodology, see Heijmans, Heuver, and Walraven (2011) and Arciero et al (2013). 21…”

Section: Data Descriptionmentioning

confidence: 99%