The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market

The topological properties of interbank networks have been discussed widely in the literature mainly because of their relevance for systemic risk. Here we propose to use the Stochastic Block Model to investigate and perform a model selection among several possible two block organizations of the network: these include bipartite, core-periphery, and modular structures. We apply our method to the e-MID interbank market in the period 2010-2014 and we show that in normal conditions the most likely network organization is a bipartite structure. In exceptional conditions, such as after LTRO, one of the most important unconventional measures by ECB at the beginning of 2012, the most likely structure becomes a random one and only in 2014 the e-MID market went back to a normal bipartite organization. By investigating the strategy of individual banks, we explore possible explanations and we show that the disappearance of many lending banks and the strategy switch of a very small set of banks from borrower to lender is likely at the origin of this structural change.Comment: 33 pages, 5 figure

Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis

Interbank markets are often characterised in terms of a core-periphery network structure, with a highly interconnected core of banks holding the market together, and a periphery of banks connected mostly to the core but not internally. This paradigm has recently been challenged for short time scales, where interbank markets seem better characterised by a bipartite structure with more core-periphery connections than inside the core. Using a novel core-periphery detection method on the eMID interbank market, we enrich this picture by showing that the network is actually characterised by multiple core-periphery pairs. Moreover, a transition from core-periphery to bipartite structures occurs by shortening the temporal scale of data aggregation. We further show how the global financial crisis transformed the market, in terms of composition, multiplicity and internal organisation of core-periphery pairs. By unveiling such a fine-grained organisation and transformation of the interbank market, our method can find important applications in the understanding of how distress can propagate over financial networks.Comment: 17 pages, 9 figures, 1 tabl

Loan maturity aggregation in interbank lending networks obscures mesoscale structure and economic functions

Since the 2007-2009 financial crisis, substantial academic effort has been dedicated to improving our understanding of interbank lending networks (ILNs). Because of data limitations or by choice, the literature largely lacks multiple loan maturities. We employ a complete interbank loan contract dataset to investigate whether maturity details are informative of the network structure. Applying the layered stochastic block model of Peixoto (2015) and other tools from network science on a time series of bilateral loans with multiple maturity layers in the Russian ILN, we find that collapsing all such layers consistently obscures mesoscale structure. The optimal maturity granularity lies between completely collapsing and completely separating the maturity layers and depends on the development phase of the interbank market, with a more developed market requiring more layers for optimal description. Closer inspection of the inferred maturity bins associated with the optimal maturity granularity reveals specific economic functions, from liquidity intermediation to financing. Collapsing a network with multiple underlying maturity layers or extracting one such layer, common in economic research, is therefore not only an incomplete representation of the ILN's mesoscale structure, but also conceals existing economic functions. This holds important insights and opportunities for theoretical and empirical studies on interbank market functioning, contagion, stability, and on the desirable level of regulatory data disclosure

The impact of the negative interest rate policy on bank´s profitability : the portuguese experience

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceA maior herança da Grande Recessão (crise financeira de 2007/08 e crise das hipotecas subprime dos EUA de 2007/09) é definitivamente a queda da indústria bancária e a incapacidade dos países de reembolsar a sua dívida soberana e aumentar o seu PIB. As ligações são inegáveis e os Bancos Centrais foram responsáveis por uma resposta rápida para reverter essa queda a pique. Esta dissertação pretende analisar o efeito de taxas de juro baixas acrescido de uma política específica adotada pelo Banco Central Europeu (BCE), a saber, a Política de Taxas de Juros Negativos (Negative Interest Rate Policy - NIRP) na rentabilidade dos bancos em Portugal. Em essência, o principal objetivo desta dissertação é entender como a Política de Taxas de Juros Negativas moldaram o setor bancário em Portugal. Identificamos e analisámos os cinco principais canais pelos quais o NIRP impacta a rentabilidade dos bancos, nomeadamente o Canal de Taxa de Juros, o Canal de Crédito, o Canal de Carteira de Ativos, o Canal de Reflação e o Canal de Câmbio. Utilizámos modelos de Regressão Linear Múltipla combinados com uma Regressão Stepwise para identificar as variáveis mais significativas na explicação da rentabilidade e desempenho dos bancos. Este método é comumente usado em estudos similares. Considerámos múltiplas variáveis explicativas, incluindo taxas de juro diretoras do BCE (taxas de facilidade permanente de depósito e de facilidade permanente de cedência marginal de liquidez), taxas de juros do mercado monetário interbancário, variáveis específico do setor financeiro (por exemplo, rácio custo / rendimento, rácio Crédito / Depósito) e variáveis macroeconómicas (Crescimento real do PIB, taxa de desemprego). Recorremos a dados publicamente disponíveis, para 35 bancos diferentes, de 2010 a 2017, fornecidos pela Associação Portuguesa de Bancos (APB), pelo Banco de Portugal (BdP), pelo BCE e pelo Instituto Europeu para os Mercados Monetários (EMMI). Durante este período, os bancos portugueses fizeram algumas mudanças nas suas estratégias de negócio, aumentando o foco nas comissões e comissões de serviço e maiores retornos da gestão de carteiras. Depois de executar os modelos e analisar os resultados, podemos concluir que quando o BCE decidiu utilizar o NIRP, como forma de recuperar a economia europeia, os canais que mais afetaram a rentabilidade do banco português foram o Canal de Taxa de Juro, o Canal de Crédito e o Canal de Carteira de Ativos.The aftermath of the Great Recession (financial crisis of 2007/08 and U.S. subprime mortgage crisis of 2007/09) and the Euro Zone Sovereign Debt Crisis is definitely the fall of the Banking industry and the countries incapability of repaying their debts. The world economy suffered a major setback and Governments and Central Banks had to provide actions to regain the financial strength they once had. A quick response was demanded in order to reverse this tsunami of downfalls that jeopardized the economical actors. This paper intends to analyse the effects of negative interest rates plus a specific policy adopted by the European Central Bank (ECB), namely the Negative Interest Rates Policy (NIRP), on banks’ profitability in Portugal. We identified and analysed the five main channels by which NIRP impacts on banks’ profitability, namely the Interest Rate Channel, the Credit Channel, the Portfolio Channel, the Reflation Channel and the Exchange Rate Channel. We used Multiple Linear Regression models combined with a Stepwise Regression to identify the most significant variables in explaining bank's profitability and performance. This method is commonly used in similar related studies. We considered multiple explanatory variables, including ECB key interest rates (deposit and facility rates), Interbank Money Market Interest Rates, Bank Specific covariates (e.g., Cost-to-Income ratio, Loan-to-Deposit ratio) and macroeconomic variables (e.g., real GDP Growth, unemployment rate). We use publicly available data for 35 different banks from 2010 to 2017 provided by Portuguese Banking Association (Associação Portuguesa de Bancos, APB), Bank of Portugal (Banco de Portugal, BdP), ECB and European Money Markets Institute (EMMI). During this period Portuguese banks made some changes in their business strategies, increasing the focus on servicing fees and commissions and higher returns from portfolio management. After executing the models and analysing the results, we can conclude that when ECB decided to use NIRP, as a mean to recover the European economy, the channels that most affected Portuguese bank’s profitability, were the Interest Rate Channel, the Credit Channel and the Portfolio Channel

Detecting Core-Periphery Structures by Surprise

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 connectivity is significantly large. Communities, however do not represent the only kind of mesoscale structures characterizing real-world networks: other examples are provided by bow-tie structures, core-periphery structures and bipartite structures. Here we propose a novel method to detect statistically-signifcant 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) just between the (empty) 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.Comment: 11 pages, 10 figures. Python code freely available at https://github.com/jeroenvldj/bimodular_surpris

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

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.Comment: 19 pages, 6 figure