A multiplex financial network approach to policy evaluation: the case of euro area Quantitative Easing

Abstract

Over the last decades, both advanced and emerging economies have experienced astriking increase in the intra-financial activity across different asset classes and increasingly complex contract types, leading to a far more complex financial system. Until the 2007-2008 crisis, the increased financial intensity and complexity was believed beneficial in making the financial system more resilient and less vulnerable to shocks. However, in 2007-2008, the advanced economies suffered the biggest financial crisis since the 1930s, followed by a severe post-crisis recession, questioning the adequacy of traditional tools in predicting, explaining, and responding to periods of financial distress. In particular, the effect of complex interconnections among financial actors on financial stability has been widely acknowledged. A recent debate focused on the effects of unconventional policies aimed at achieving both price and financial stability. Among these unconventional policies, Quantitative Easing (QE, i.e., the large-scale asset purchase programme conducted by a central bank upon the creation of new money) has been recently implemented by the European Central Bank (ECB). In this context, two questions deserve more attention in the literature. First, to what extent, the resources provided to the banking system through QE are transmitted to the real economy. Second, to what extent, the QE may also alter the pattern of intra-financial exposures and what are the implications in terms of financial stability. Here, we address these two questions by developing a methodology to map the multilayer macro-network of financial exposures among institutional sectors across financial instruments (i.e., loans and deposits, debt securities, and equity), and we illustrate our approach on recently available data. We then test the effect of the implementation of ECB’s QE on the time evolution of the financial linkages in the multilayer macro-network of the euro area, as well as the effect on macroeconomic variables, such as consumption, investment, unemployment, growth, and inflation.Electronic supplementary materialThe online version of this article (10.1007/s41109-018-0098-8) contains supplementary material, which is available to authorized users.

Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis

“…Multilayer networks have been also used to evaluate the impact of unconventional monetary policies on macroeconomic aggregates. For example, Perillo and Battiston (2018) map the multilayer macro-network of financial exposures among institutional sectors in Europe to shed light on the implications of ECB quantitative easing in terms of stimulation of the real economy: they find that the resources provided to the banking system through quantitative easing have been transmitted mainly to the financial sector.…”

Financialization and unconventional monetary policy: a financial-network analysis

“…In fact, this approach enables one to account for the relevant direct and indirect exposures via the different financial instruments considered together and provides a map of the potential channels through which distress may propagate as a consequence of shocks of different nature. Further, Perillo and Battiston (2018) provide empirical evidence supporting the fact that these insights could not be obtained with a single-layer analysis, and that, by looking at a single layer at a time, one may fail to detect instability, underestimating the systemic importance and the vulnerability of the nodes in the network.…”

“…Despite the fact that the multilayer micro-network would be the ideal way of regarding the economy, granular data on financial exposures are not always available. In this regard, a recent work by Castrén and Rancan (2014) has introduced the concept of macro-network of financial exposures (recently revisited by Perillo and Battiston (2017); Perillo and Battiston (2018); Stolbova et al (2018)), where nodes are the institutional sectors operating in the economy and links are the financial contracts outstanding among them. This macro-network is the result of an aggregation by institutional sector, meaning that macro-nodes (i.e., the institutional sectors) represent an aggregation of the micro-networks of financial exposures outstanding among the institutions or individuals operating within the specific institutional sectors.…”

“…Moreover, according to the same argument made for the micro-network (i.e., financial contracts among institutions or individuals, and thus, among institutional sectors, occur through different kinds of financial instruments), the macro-network of financial exposures may be regarded as a multilayer network of financial exposures, where layers correspond to different financial instruments. In this regard, Perillo and Battiston (2018) argue that the analysis of the euro area economy as a multilayer financial network allows one to obtain relevant insights on the challenging problem of estimating the financial interdependencies existing between the banking sector and both the real and the financial sectors. In fact, this approach enables one to account for the relevant direct and indirect exposures via the different financial instruments considered together and provides a map of the potential channels through which distress may propagate as a consequence of shocks of different nature.…”

Fragility of a multilayer network of intranational supply chains

“…Network science approaches have been used to analyze the interplay between structure and shock dynamics in various types of economic networks (Fagiolo 2016 ; Schweitzer et al 2009 ), such as financial (Battiston et al 2016 ; Delpini et al 2019 ; Perillo and Battiston 2018 ; Starnini et al 2019 ), firm (Hisano et al 2017 ; Joyez 2017 ), trade (Bonaccorsi et al 2019 ; Garlaschelli and Loffredo 2004 ; Serrano and Boguñá 2003 ), and supply-chain (Inoue and Todo 2019 ; Matous and Todo 2017 ; Perera et al 2017 ; Piccardi et al 2017 ) networks. Our focus in this study is on supply-chain networks as represented by economic input-output data.…”