Special Issue "Granger Causality and Transfer Entropy for Financial Networks"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 20 July 2022.

Special Issue Editor

Dr. Angeliki Papana
E-Mail Website
Guest Editor
1. Department of Economics, University of Macedonia, Egnatias 156, 546 36 Thessaloniki, Greece
2. Polytechnic School, Aristotle University of Thessaloniki, University Campus, 541 24 Thessaloniki, Greece
Interests: time series analysis; Granger causality; complex networks; Monte Carlo simulations; resampling methods; dynamical systems; chaos
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Causality is the relationship between cause and effect. Granger causality is a probabilistic account of causality which provides a way to investigate causality in terms of prediction. Granger causality has been a leading concept for decades in the field of finance; however, its notion has also been utilized in many other fields, such as neurophysiology, meteorology, and seismography.

The original bivariate Granger causality concept has been vastly extended. Transfer entropy is a nonlinear generalization of the Granger causality test stemming from information theory, and it is therefore model-free and accounts for both linear and nonlinear causal effects. Extensions of Granger causality and transfer entropy include causality measures in phase space, multivariate causality measures, and dimension reduction causality measures.

Methods of complex networks, in conjunction with graph theory, offer an effective way of understanding and visualizing the relationships between variables in the case of complex systems, by representing the involved variables as nodes and the interactions as edges in the graph.

Financial data have specific features that have been thoroughly studied, such as nonnormality, volatility clustering, and nonlinearities. Therefore, to infer about the relationships of financial variables and correctly attain the connectivity network, suitable approaches that take into consideration the features of the data are required.

The scope of this Special Issue is to provide insights on the causal relationships of financial networks, including theoretical, methodological, and empirical works, such as methodological innovations on the estimation of causal measures, representation/visualization of financial networks, understanding how financial networks amplify shocks, modeling the heterogeneity of interconnections, and understanding the evolution of financial networks and its impact on systemic risk and financial stability.

Dr. Angeliki Papana
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Granger causality
  • transfer entropy
  • connectivity
  • information theory
  • complex networks
  • finance

Published Papers (4 papers)

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Research

Article
Information Flow Network of International Exchange Rates and Influence of Currencies
Entropy 2021, 23(12), 1696; https://0-doi-org.brum.beds.ac.uk/10.3390/e23121696 - 18 Dec 2021
Viewed by 370
Abstract
The main purpose of the study is to investigate how price fluctuations of a sovereign currency are transmitted among currencies and what network traits and currency relationships are formed in this process under the background of economic globalization. As a universal equivalent, currency [...] Read more.
The main purpose of the study is to investigate how price fluctuations of a sovereign currency are transmitted among currencies and what network traits and currency relationships are formed in this process under the background of economic globalization. As a universal equivalent, currency with naturally owned network attributes has not been paid enough attention by the traditional exchange rate determination theories because of their overemphasis of the attribute of value measurement. Considering the network attribute of currency, the characteristics of the information flow network of exchange rate are extracted and analyzed in order to research the impact they have on each other among currencies. The information flow correlation network between currencies is researched from 2007 to 2019 based on data from 30 currencies. A transfer entropy is used to measure the nonlinear information flow between currencies, and complex network indexes such as average shortest path and aggregation coefficient are used to analyze the macroscopic topology characteristics and key nodes of information flow-associated network. It was found that there may be strong information exchange between currencies when the overall market price fluctuates violently. Commodity currencies and currencies of major countries have great influence in the network, and local fluctuations may result in increased risks in the overall exchange rate market. Therefore, it is necessary to monitor exchange rate fluctuations of relevant currencies in order to prevent risks in advance. The network characteristics and evolution of major currencies are revealed, and the influence of a currency in the international money market can be evaluated based on the characteristics of the network. The world monetary system is developing towards diversification, and the currency of developing countries is becoming more and more important. Taking CNY as an example, it was found that the international influence of CNY has increased, although without advantage over other major international currencies since 2015, and this trend continues even if there are trade frictions between China and the United States. Full article
(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)
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Article
Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
Entropy 2021, 23(12), 1570; https://0-doi-org.brum.beds.ac.uk/10.3390/e23121570 - 25 Nov 2021
Viewed by 349
Abstract
The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the [...] Read more.
The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance. Full article
(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)
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Article
The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test
Entropy 2021, 23(9), 1172; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091172 - 06 Sep 2021
Viewed by 530
Abstract
Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between [...] Read more.
Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between WTI crude oil futures market and Chinese stock index futures market or US stock index futures market. The relationship in mean between WTI crude oil futures prices and S&P 500 stock index futures, or CSI 300 stock index futures is weakening. Meanwhile, there is a decreasing dynamic conditional correlation between the WTI crude oil futures market and Chinese stock index futures market or US stock index futures market after the breakpoint in the price series. The Chinese stock index futures are less affected by short-term fluctuations in crude oil futures returns than US stock index futures. Full article
(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)
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Article
On the Dynamics of International Real-Estate-Investment Trust-Propagation Mechanisms: Evidence from Time-Varying Return and Volatility Connectedness Measures
Entropy 2021, 23(8), 1048; https://0-doi-org.brum.beds.ac.uk/10.3390/e23081048 - 14 Aug 2021
Viewed by 643
Abstract
In this paper, we investigate the time-varying interconnectedness of international Real Estate Investment Trusts (REITs) markets using daily REIT prices in twelve major REIT countries since the Global Financial Crisis. We construct dynamic total, net total and net pairwise return and volatility connectedness [...] Read more.
In this paper, we investigate the time-varying interconnectedness of international Real Estate Investment Trusts (REITs) markets using daily REIT prices in twelve major REIT countries since the Global Financial Crisis. We construct dynamic total, net total and net pairwise return and volatility connectedness measures to better understand systemic risk and the transmission of shocks across REIT markets. Our findings show that that REIT market interdependence is dynamic and increases significantly during times of heightened uncertainty, including the COVID-19 pandemic. We also find that the US REIT market along with major European REITs are generally sources of shocks to Asian-Pacific REIT markets. Furthermore, US REITs appear to dominate European REITs. These findings highlight that portfolio diversification opportunities decline during times of market uncertainty. Full article
(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)
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