I am pleased to introduce the second issue of Volume 5 of The Journal of Network Theory in Finance, which offers three very interesting contributions that I am sure you will enjoy reading.
In particular, two of them were handled, in his capacity as a guest editor, by my colleague and friend Serafın Martınez Jaramillo of Banco de Mexico – who is also a member of our prestigious editorial board. I thank him hugely for his contribution.
The issue starts with a paper by Paolo Giudici and Gloria Polinesi titled “Scoring models for roboadvisory platforms: a network approach”, which contributes to the financial technology (FinTech) arena by suggesting improvements for financial advice services. Automated digital consultancy platforms (“robot advisors”) reduce costs and improve perceived service quality, speeding it up and making user involvement more transparent. These improvements are often offset by risk classification models that are simpler than those employed in traditional consultancy. The authors show how to exploit the available data to build portfolios that better fit the risk pro- files of investors. In particular, robot advisors generate, in an automated way, a large amount of (big) data that can be leveraged not only to improve the service – making it more personalized, as is customarily done – but also to reduce risks, particularly that of an incorrect risk profile matching between “expected” and “actual” risk classes. The paper proposes a data-driven methodology that classifies investors and financial products into risk-homogeneous classes and shows how this can be done in practice. It can be achieved through a combination of machine learning tools and filtering network techniques such as the minimum spanning tree and the hierarchical cluster procedure.
In “Interdependencies in the euro area derivatives clearing network: a multi- layer network approach”, our second paper, authors Simonetta Rosati and Francesco Vacirca benefit enormously from a unique data set. In addition, by using the multi- layer approach, they provide valuable insights into the systemic relevance of inter- dependencies for some of the participants in this market. The authors are able to identify relevant participants, taking into account the information provided by the multiplex network. The most important finding of this paper is that using a multiplex approach reveals aspects that would be ignored if only individual layers were considered, or if all the different layers were aggregated into a single network. Discussions about the benefits of having central counterparties for the most important derivatives classes are ongoing. This study is more relevant now than ever before in helping us to monitor the possible buildup of systemic risk in this important segment of the financial system.
The issue’s third and final paper, “Mapping bank securities across euro area sectors: comparing funding and exposure networks” by Anne-Caroline Huser and Christoffer Kok, represents an important contribution to the empirical literature on multiplex financial networks. The authors cover different important aspects of the multiplex interbank network as well as the extended financial network in the euro area. Using a multilayer network approach, they identify important systemic risks: counterparty risk, concentration risk and funding risk. Moreover, the authors find that while layers exhibit very different properties, global systemically important banks (G-SIBs) exhibit similar activities across layers. Nevertheless, there are other banks that demonstrate high levels of interconnectedness in the multiplex structure. This work benefits from unique data sets at the European level and makes good use of them, studying not one but three different multilayer structures of the macrofinan- cial multiplex network. The authors show that it is very beneficial to have low- granularity data; this is even more important considering the recent growth of the shadow banking system.
Tiziana Di Matteo
King’s College London
In this paper, the authors show how to exploit the available data to build portfolios that better fit the risk profiles of investors. This is made possible, on the one hand, by constructing groups of homogeneous risk profiles based on user responses to…
This paper provides insight into how the collected data pursuant to the EMIR can be used to shed light on the complex network of interrelations underlying the financial markets.
In this paper, the authors present new evidence on the structure of euro area securities markets using a multilayer network approach.