Journal of Network Theory in Finance
ISSN:
2055-7795 (print)
2055-7809 (online)
Editor-in-chief: Ron Berndsen
Volume 2, Number 3 (September 2016)
Editor's Letter
On September 13 and 14, 2016, the third annual conference on Network Theory and Financial Risk, sponsored by The Journal of Network Theory in Finance, was held at the Centre for Risk Studies at Cambridge University. The conference is unique in bringing together academics, regulators and industry practitioners to discuss and share knowledge about financial networks. The program contained nine keynotes and invited talks from members of the journal's editorial board. The thirty-three research papers presented over two days used methods from network theory to address systemic risk, and discussed market risk and asset allocation strategies as well as risks stemming from interbank exposures. Multilayer and multiplex networks featured heavily in this year's program as a key new area of research, along with market-based methods for measuring systemic risk. The conference was larger than the one in 2015 in terms of participants and research presented. The second day also featured an industry panel with leading industry executives discussing "Networks and evolving analytics demands in large financial institutions". The next conference will be held in September 2017.
This issue contains three papers. The first, "NetMES: a network based marginal expected shortfall measure" by Shatha Qamhieh Hashem and Paolo Giudici, develops a new measure for systemic risk based on the asset return series of financial institutions. The authors apply this measure to the Gulf Cooperation Council countries' different banking sectors and find increased interconnectedness after the financial crisis, where the most systemic nodes in the network are conventional banks with Islamic services windows. Market-based methods for systemic risk measurement are an important addition to the extensive work carried out by regulators to measure direct links and channels of contagion through exposure and trade repository data. They provide a baseline and allow market participants to tackle systemic risk, as such data is not confidential.
The issues's second paper, "A multilayer model of order book dynamics" by Alessio Emanuele Biondo, Alessandro Pluchino and Andrea Rapisarda, builds a simulation model for market price formation. The model, which borrows from similar models built within the field of statistical mechanics, represents information spreading and trading as separate layers in a multiplex network. In the model, realistic price formation emerges from the dynamical interaction among traders. Models such as these help us to understand and explain the often overlooked aspect of how small changes in the microstructure of the market can have large effects on its macroscopic behavior.
Our third paper, "Directors' networks and firm valuation in a concentrated ownership structure economy" by Ronen Barak and Oren Kapah, contributes to the active literature on overlapping directors'networks, and measures the impact of the network position of different types of directors on firm performance. The authors find that professional directors in a sample of 727 listed Israeli firms with high centrality promote firm valuation, while central external directors have a negative effect. The results are of interest to investors, policy makers and regulators, whose agency problems may be mitigated by strong independent boards.
Kimmo Soramäki and Tiziana Di Matteo
Financial Network Analytics Ltd. and King's College London
Papers in this issue
Directors’ networks and firm valuation in a concentrated ownership structure economy
The authors explore the implications of directors' networks for company valuation in a concentrated ownership environment and in pyramidal control structures.
A multilayer model of order book dynamics
This paper presents a two-layer order book model.
NetMES: a network based marginal expected shortfall measure
This paper aims to build novel measures of systemic risk that take the multivariate nature of the problem into account by means of network models.