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.2489875

Cited by 39 publications

(75 citation statements)

References 10 publications

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“…Regarding the validity of the Furfine's method, Armantier and Copeland (2012) have questioned the results obtained when implementing Furfine's method. On the other hand, Arciero et al (2013) contrast results reported by Armantier and Copeland, and confirm the validity of Furfine's method conditional on a deep knowledge of the underlying data and the technical attributes of the system under analysis.…”

Section: Introductionsupporting

confidence: 76%

“…This feature of the dataset avoids making some assumptions for identifying loans and refunds. For instance, following the algorithm's setup by Arciero et al (2013), we do not need to define minimum loan values and increments in loan values to filter out potential loans. As defining these two parameters is critical for minimizing false negatives and false positives (Arciero et al, 2013), working with filtered data should mitigate some serious sources of error.…”

Section: The Algorithmmentioning

confidence: 99%

“…greater than $50,000, with equal-sized increments) and include an interest rate 1 32 ⁄ -rounding rule for discarding interbank overnight transactions that are incompatible with market practices. Millard and Polenghi (2004) and Euro interbank markets (see Heijmans et al, 2010;Guggenheim, Kraenzlin, & Schumacher, 2010;Arciero, Heijmans, Heuver, Massarenti, Picillo, & Vacirca, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Identifying interbank loans, rates, and claims networks from transactional data

León-Rincón¹,

Cely²,

Cadena-Silva³

et al. 2015

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We identify interbank (i.e. non-collateralized) loans from the Colombian large-value payment system by implementing Furfine's method. After identifying interbank loans from transactional data we obtain the interbank rates and claims without relying on financial institutions' reported data. Contrasting identified loans with those consolidated from financial institutions' reported data suggests the algorithm performs well, and it is robust to changes in its setup. The weighted average rate implicit in transactional data matches local interbank rate benchmarks strictly. From identified loans we also build the interbank claims network. The three main outputs (i.e. the interbank loans, the rates, and the claims networks) are valuable for examining and monitoring the money market, for contrasting data reported by financial institutions, and as inputs in models of financial contagion and systemic risk.

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Section: Introductionsupporting

confidence: 76%

Section: The Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying interbank loans, rates, and claims networks from transactional data

León-Rincón¹,

Cely²,

Cadena-Silva³

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The success of the Furfine algorithm at identifying interbank lending has been studied for a number of markets, including the Bank of England's CHAPS Sterling settlement system (cf Wetherilt et al, 2009 andAcharya and. Merrouche, 2013), Switzerland(Guggenheim and Kraenzlin, 2010), and the Eurosystem real-time gross settlement system, TARGET2(Arciero et al, 2013).…”

mentioning

confidence: 99%

Efficiency and Bargaining Power in the Interbank Loan Market

Allen

Chapman

Echenique

et al. 2016

Int Economic Review

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Using detailed loan transactions-level data we examine the efficiency of an overnight interbank lending market, and the bargaining power of its participants. Our analysis relies on the equilibrium concept of the core, which imposes a set of no-arbitrage conditions on trades in the market. For Canada we show that while the market is fairly efficient, some degree of inefficiency persists throughout our sample. The level of inefficiency matches distinct phases of both the Bank of Canada's operations as well as phases of the 2007-2008 financial crisis, where more liquidity intervention implies more inefficiency. We find that bargaining power tilted sharply towards borrowers as the financial crisis progressed, and towards riskier borrowers. This supports a nuanced version of the TooBig-To-Fail story, whereby participants continued to lend to riskier banks at favorable rates, not because of explicit support to the riskier banks provided by governmental authorities, but rather due to the collective self-interest of these banks. JEL classification: C71, G21, G28, E58Bank classification: Financial institutions; Payment, clearing, and settlement systems Résumé À l'aide de données détaillées sur les transactions relatives aux prêts, les auteurs examinent l'efficience du marché du financement interbancaire à un jour ainsi que le pouvoir de négociation de ses participants. L'analyse repose sur ce concept d'équilibre qu'est le coeur, qui impose un ensemble de conditions de non-arbitrage aux opérations du marché. L'étude montre que même si, au Canada, ce marché fonctionne de façon efficace, il se caractérise par un certain degré d'inefficience tout au long de la période considérée. Les variations du degré d'inefficience correspondent à des phases distinctes tant des opérations de la Banque du Canada que de la crise financière de 2007-2008, des injections accrues de liquidité ayant pour corollaire une plus grande inefficience. Les auteurs constatent par ailleurs que la balance du pouvoir de négociation a penché très nettement durant la crise du côté des institutions emprunteuses, en particulier du côté des emprunteurs à risque. Cela tend à confirmer une version nuancée d'une hypothèse bien connue, à savoir que l'État vole au secours des banques en difficulté lorsque celles-ci sont trop grosses ou trop complexes (too big to fail). Selon cette version, les participants auraient continué d'octroyer des prêts à taux favorables aux banques à risque, non du fait d'un soutien explicite des autorités à ces banques, mais surtout parce que cet octroi était dans l'intérêt collectif des prêteurs.

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“…Most of them are settled as over-thecounter (OTC) agreements, about which data is not systematically collected. Studies on interbank loan markets therefor often rely on data that is reconstructed from the observation of payments between banks (see, e.g., Arciero et al, 2013). The data that we analyze here is not from OTC transactions.…”

Section: Introductionmentioning

confidence: 99%

Cascades in Real Interbank Markets

Karimi¹,

Raddant²

2014

Comput Econ

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We analyze cascades of defaults in an interbank loan market. The novel feature of this study is that the network structure and the size distribution of banks are derived from empirical data. We find that the ability of a defaulted institution to start a cascade depends on an interplay of shock size and connectivity. Further results indicate that the ability to limit default risk by spreading the lending to many counterparts decreased with the financial crisis. To evaluate the influence of the network structure on market stability, we compare the simulated cascades from the empirical network with results from different randomized network models. The results show that the empirical network has non-random features, which cannot be captured by rewired networks. The analysis also reveals that simulations assuming homogeneity for size of banks and loan contracts dramatically overestimates the fragility of the interbank market. keywords: interbank loan network -systemic risk -cascades -null models arXiv:1310.1634v2 [q-fin.ST]

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

Cited by 39 publications

References 10 publications

Identifying interbank loans, rates, and claims networks from transactional data

Identifying interbank loans, rates, and claims networks from transactional data

Efficiency and Bargaining Power in the Interbank Loan Market

Cascades in Real Interbank Markets

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