Complexity

Financial Networks 2019


Publishing date
01 Aug 2019
Status
Published
Submission deadline
29 Mar 2019

1Escola de Políticas Públicas e Governo, Fundação Getúlio Vargas, Brasília, Brazil

2Central Bank of Brazil, Brasília, Brazil

3Bilkent University, Ankara, Turkey


Financial Networks 2019

Description

Financial networks have been on the research agenda since the financial crisis of 2008. Today, both regulators and the academia recognize that interconnectedness is a crucial component that had a key role in the amplification of losses in the last crisis. Therefore, understanding the structure of financial networks is important for assessing, monitoring, and regulating financial systems. In addition, it washed away the belief that supervising banks in an individual manner was sufficient to guarantee a safe financial system, as networks can largely amplify negative spillover effects. In this sense, we have seen an increasing effort in designing novel mechanisms for macroprudential regulation that include overseeing aspects of the entire financial system, including its structure.

Though understanding how financial networks amplify shocks is of uttermost importance for policymakers, especially for financial stability and systemic risk issues, the literature is still at its early stages in understanding the role of financial networks as a medium for shock amplification. This mainly occurs because modern financial networks are inherently complex to analyze as economic agents participate in a multiplex of interrelationships in several different markets.

Modeling this heterogeneity of interconnections stands as an important open problem because each connection can potentially create contagion transmission channels that can amplify losses. Another component that further increases the modeling complexity is that each risk channel that arises from this multiplex of interconnections is potentially dependent on each other and thus can additively increase systemic risk in nonlinear ways.

Alternative modeling that uses methods from chaos theory, genetic algorithms, cellular automata, neural networks, and evolutionary game theory that will study the behavior of financial networks or its components is welcome.

Complex networks evolve rapidly overtime and their topology changes substantially. Understanding this evolution and its impact on systemic risk and financial stability is an important research question. There are many gaps in the literature and we hope to address some of them within this call for papers. We look for papers that contribute to the debate on complexity and financial networks.

Potential topics include but are not limited to the following:

  • Financial stability
  • Systemic risk
  • Network prediction
  • Interdependent networks
  • Cross-system risk
  • Default contagion
  • Network topology
  • Endogenous financial networks
  • Investor networks
  • Collective behaviour
  • Network resilience
  • Bayesian dynamic financial networks
  • Financial regulation
  • Multiplex networks
  • Link prediction
  • Interbank connections
  • Systemic relevance
  • Bank supervision
  • Machine learning
  • Econometrics of networks
  • Agent based modeling
  • Chaos
  • Genetic algorithms
  • Cellular automata
  • Neural networks
  • Deep learning
  • Mean field theory
  • Evolutionary game theory

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 8257404
  • - Editorial

Financial Networks 2019

Benjamin Miranda Tabak | Thiago Christiano Silva | Ahmet Sensoy
  • Special Issue
  • - Volume 2019
  • - Article ID 3582516
  • - Research Article

Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Taewook Kim | Ha Young Kim
  • Special Issue
  • - Volume 2019
  • - Article ID 9202457
  • - Research Article

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

Yang Liu | Qingguo Zeng | ... | Huanrui Yang
  • Special Issue
  • - Volume 2019
  • - Article ID 7490640
  • - Research Article

Analyzing Stock Brokers’ Trading Patterns: A Network Decomposition and Spatial Econometrics Approach

Juan Eberhard | Jaime F. Lavín | ... | José Arenas
  • Special Issue
  • - Volume 2019
  • - Article ID 7820618
  • - Research Article

Weight of the Default Component of CDS Spreads: Avoiding Procyclicality in Credit Loss Provisioning Framework

Mariya Gubareva
  • Special Issue
  • - Volume 2019
  • - Article ID 1715624
  • - Research Article

Understanding How Short-Termism and a Dynamic Investor Network Affects Investor Returns: An Agent-Based Perspective

Matthew Oldham
  • Special Issue
  • - Volume 2019
  • - Article ID 1565698
  • - Research Article

Modeling Overlapped Mutual Funds’ Portfolios: A Bipartite Network Approach

Jaime F. Lavin | Mauricio A. Valle | Nicolás S. Magner
  • Special Issue
  • - Volume 2019
  • - Article ID 2946018
  • - Research Article

Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network

Binghui Wu | Tingting Duan
  • Special Issue
  • - Volume 2019
  • - Article ID 2989204
  • - Research Article

Control Strategy for a Fractional-Order Chaotic Financial Model

Changjin Xu | Maoxin Liao | ... | Shuai Yuan
  • Special Issue
  • - Volume 2019
  • - Article ID 1817248
  • - Research Article

Thermodynamic Entropy in Quantum Statistics for Stock Market Networks

Jianjia Wang | Chenyue Lin | Yilei Wang
Complexity
 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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