Quantifying Preferential Trading in the e-MID Interbank Market

Hatzopoulos, Vasilis; Iori, Giulia; Mantegna, Rosario N.; Miccichè, Salvatore; Tumminello, Michele

doi:10.2139/ssrn.2343647

Cited by 29 publications

(73 citation statements)

References 38 publications

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“…Preferential trading between banks can be detected by comparing empirically observed trading relationships with a null hypothesis that assumes random trading. Hatzopoulos et al [24] have introduced a statistical test to assess the statistical significance of the observed interbank transactions in order to reveal preferential Figure 5. Fraction of statistically validated links according to the test in Hatzopoulos et al [24] conditional to the value of the estimated α ij parameter measuring the link persistence in the TGRG-DAR model.…”

Section: 3mentioning

confidence: 99%

“…Preferential trading relations i → j are the ones rejected according to the statistical test, i.e. with a p-value smaller than the threshold value 0.05 a where a is the Bonferroni correction to avoid a large number of false positive validated links because of the multiple hypothesis testing (see [24] for more details). Figure 5 shows the frequency of rejection for the statistical test of Ref.…”

Section: 3mentioning

confidence: 99%

“…Hence, we apply our methodology to the financial network of electronic Market of Interbank Deposit (e-MID) where the two linkage mechanisms are associated with two different trading behaviors, i.e. random and preferential trading [24], in the process of network formation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

Mazzarisi

Barucca

Lillo

et al. 2020

European Journal of Operational Research

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We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognise preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.

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Section: 3mentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

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A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

Mazzarisi

Barucca

Lillo

et al. 2020

European Journal of Operational Research

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“…In order to analyze communication data for constructing MCR based networks, one has to first decide whether the entries in the records serve as good proxies for real social interactions in a probabilistic sense. This is a multiple-hypothesis test validation problem, which we approach by adapting and applying a directional version [32] of the recently introduced method of Bonferroni networks [33].…”

Section: The Bonferroni Network Of Mobile Call Recordsmentioning

confidence: 99%

Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data

Palchykov

Jiang

et al. 2014

New J. Phys.

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Big data open up unprecedented opportunities to investigate complex systems including the society. In particular, communication data serve as major sources for computational social sciences but they have to be cleaned and filtered as they may contain spurious information due to recording errors as well as interactions, like commercial and marketing activities, not directly related to the social network. The network constructed from communication data can only be considered as a proxy for the network of social relationships. Here we apply a systematic method, based on multiple hypothesis testing, to statistically validate the links and then construct the corresponding Bonferroni network, generalized to the directed case. We study two large datasets of mobile phone records, one from Europe and the other from China. For both datasets we compare the raw data networks with the corresponding Bonferroni networks and point out significant differences in the structures and in the basic network measures. We show evidence that the Bonferroni network provides a better proxy for the network of social interactions than the original one. By using the filtered networks we investigated the statistics and temporal evolution of small directed 3-motifs and conclude that closed communication triads have a formation time-scale, which is quite fast and typically intraday. We also find that open communication triads preferentially evolve to other open triads with a higher fraction of reciprocated calls. These stylized facts were observed for both datasets.

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“…e-MID, the electronic Italian Interbank Market. Here, we compare two different datasets: the first one collects the 2005-2010 interbank transactions during the so-called maintenance periods [25]; the second one collects interbank transactions on a daily basis from 1999 to 2012 [13,14]. The main difference between the two datasets lies in their level of aggregation: notice, in fact, that the first one basically collects data on a monthly basis.…”

Section: Resultsmentioning

confidence: 99%

Detecting core-periphery structures by surprise

Jeude¹,

Caldarelli²,

Squartini³

2019

EPL

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Detecting the presence of mesoscale structures in complex networks is of primary importance. This is especially true for financial networks, whose structural organization deeply affects their resilience to events like default cascades, shocks propagation, etc. Several methods have been proposed, so far, to detect communities, i.e. groups of nodes whose internal connectivity is significantly large. Communities, however do not represent the only kind of mesoscale structures characterizing realworld networks: other examples are provided by bow-tie structures, core-periphery structures and bipartite structures. Here we propose a novel method to detect statistically-significant bimodular structures, i.e. either bipartite or core-periphery ones. It is based on a modification of the surprise, recently proposed for detecting communities. Our variant allows for bimodular nodes partitions to be revealed, by letting links to be placed either 1) within the core part and between the core and the periphery parts or 2) between the layers of a bipartite network. From a technical point of view, this is achieved by employing a multinomial hypergeometric distribution instead of the traditional, binomial hypergeometric one; as in the latter case, this allows a p-value to be assigned to any given (bi)partition of the nodes. To illustrate the performance of our method, we report the results of its application to several real-world networks, including social, economic and financial ones.

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Quantifying Preferential Trading in the e-MID Interbank Market

Cited by 29 publications

References 38 publications

A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data

Detecting core-periphery structures by surprise

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