I would like to start this fourth issue of Volume 3 by thanking Kimmo Soramäki for his valuable work during his term as co-editor-in-chief of The Journal of Network Theory in Finance. We are glad that he will continue to serve as an active member of our prestigious editorial board and will keep contributing to the success of the journal. Many thanks, Kimmo.
We have three papers in this issue, the authors of which are a mixture of academics and practitioners, showing the wide spectrum of interest of our authors. I am sure our readers will benefit from this.
The issue’s first paper, “Evaluating the role of risk networks in risk identification, classification and emergence” by Christos Ellinas, Neil Allan and Caroline Coombe, analyzes networks created due to the similarity of risks reported by fifteen firms active in the UK insurance sector. This allows the authors to identify risk groups as communities in the network, to evaluate firms’ ability to systematically evaluate risks, and to measure the systemic importance of risks. As regards the latter, the authors find that risks related to political intervention, Brexit and Scottish independence rank highest. The paper provides structure and systematizes very nicely the “interconnected risks” discussion that a number of insurance companies are currently trying to tackle.
“Interconnectedness risk and active portfolio management: the information-theoretic perspective” by Eduard Baitinger and Jochen Papenbrock, the second paper in this issue, discusses linear and nonlinear dependence-based networks and their implications for investment strategies. In particular, as a nonlinear type of dependence, the authors adopt the mutual information measure. They construct financial networks that are based on this measure and compare them with Pearson correlation-based financial networks. This paper mainly contributes to discussions on the need to consider and adopt alternative nonlinear types of dependence measures in financial network studies.
Our third and final paper, “Identifying complex core–periphery structures in the interbank market” by José Gabriel Carreño and Rodrigo Cifuentes, presents an application of the stochastic blockmodeling method – borrowed from the social network literature – to the Chilean interbank financial network. This methodology allows us to analyze the network structure and its dynamical evolution, and it provides information that could be useful to policy makers when dealing with financial stability analyses.
In particular, it helps to identify groups of banks, to order them according to whether they represent a core, a periphery or neither, and to observe how they change in time. This results in informative visualizations showing clear changes within the Chilean interbank network.
Enjoy your reading!
Tiziana Di Matteo
King’s College London
This paper presents an evaluation of how risk interdependence affects the risk management process.
This paper extensively compares mutual-information-based networks with correlation-based networks on a stand-alone basis and in the framework of active investment strategies.
This paper proposes a framework to identify the structure of a financial network and its evolution over time, and presents an application to an interbank market with complete actual data.