ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-11222018-152611


Tipo di tesi
Tesi di laurea magistrale
Autore
STELLA, ELENA
URN
etd-11222018-152611
Titolo
A discrete autoregressive model for preferential lending. Network analysis of the e-MID interbank market
Dipartimento
ECONOMIA E MANAGEMENT
Corso di studi
ECONOMICS
Relatori
relatore Prof. Fagiolo, Giorgio
Parole chiave
  • interbank market
  • network
  • preferential lending
Data inizio appello
10/12/2018
Consultabilità
Non consultabile
Data di rilascio
10/12/2088
Riassunto
The aim of my work is to inquire preferential lending behaviour within the framework of the e-MID interbank market. The present work moves its steps from high frequency trading data from the Italian electronic Market for Interbank Deposit for the period between 2009 and 2015. A network analysis is performed both at the weekly and daily aggregation level. As a first step, graph objects and corresponding adjacency matrices are generated for each time period. Therefore, a series of network statistics is presented to account both for the topological features of single graphs at each snapshot and for the evolution of those characteristics across the time period under analysis. Afterwards, data are treated in order to construct suitable datasets for the estimation of preferential lending behaviour. The process according which different actors of the interbank market manage to create trading relationships, i.e. different nodes of the interbank network make links with each other, is described as a discrete autoregressive process of order one. The estimation of the model parameters supplies information about the probability of the network structure to replicate itself at different time periods and thus provides a global measure of link persistency which is associated with preferential lending behaviour in the interbank market. Time series of the parameter are extracted from weekly and daily aggregated datasets and correlated with several network metrics. Correlational evidence seems to appear with respect to a series of relevant statistics and, most interestingly, seems even stronger when computed on subsamples of the data around LTRO operations, thus suggesting that structural changes in the topological features of the network, induced by external macro policies, are to some extent aligned with the parameter’s behaviour.
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