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Granger Causality and Transfer Entropy for Financial Networks

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 44526

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Dr. Angeliki Papana

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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
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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

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Keywords

Granger causality
transfer entropy
connectivity
information theory
complex networks
finance

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Published Papers (12 papers)

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Research

24 pages, 3764 KiB

Open AccessArticle

Bibliometric Analysis of Granger Causality Studies

by Weng Siew Lam, Weng Hoe Lam, Saiful Hafizah Jaaman and Pei Fun Lee

Entropy 2023, 25(4), 632; https://doi.org/10.3390/e25040632 - 7 Apr 2023

Cited by 7 | Viewed by 3280

Abstract

Granger causality provides a framework that uses predictability to identify causation between time series variables. This is important to policymakers for effective policy management and recommendations. Granger causality is recognized as the primary advance on the causation problem. The objective of this paper is to conduct a bibliometric analysis of Granger causality publications indexed in the Web of Science database. Harzing’s Publish or Perish and VOSviewer were used for performance analysis and science mapping. The first paper indexed was published in 1981 and there has been an upward trend in the annual publication of Granger causality studies which are shifting towards the areas of environmental science, energy, and economics. Most of the publications are articles and proceeding papers under the areas of business economics, environmental science ecology, and neurosciences/neurology. China has the highest number of publications while the United States has the highest number of citations. England has the highest citation impact. This paper also constructed country co-authorship, co-analysis of cited references, cited sources, and cited authors, keyword co-occurrence, and keyword overlay visualization maps. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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25 pages, 2869 KiB

Open AccessArticle

Detecting Nonlinear Interactions in Complex Systems: Application in Financial Markets

by Akylas Fotiadis, Ioannis Vlachos and Dimitris Kugiumtzis

Entropy 2023, 25(2), 370; https://doi.org/10.3390/e25020370 - 17 Feb 2023

Cited by 4 | Viewed by 2180

Abstract

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H₀) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H₀; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H₀, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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26 pages, 2437 KiB

Open AccessArticle

Improving the Process of Early-Warning Detection and Identifying the Most Affected Markets: Evidence from Subprime Mortgage Crisis and COVID-19 Outbreak—Application to American Stock Markets

by Heba Elsegai

Entropy 2023, 25(1), 70; https://doi.org/10.3390/e25010070 - 30 Dec 2022

Cited by 1 | Viewed by 2099

Abstract

Stock-market-crash predictability is of particular interest in the field of financial time-series analysis. Famous examples of major stock-market crashes are the real-estate bubble in 2008 and COVID-19 in 2020. Several studies have studied the prediction process without taking into consideration which markets might be falling into a crisis. To this end, a combination analysis is utilized in this manuscript. Firstly, the auto-regressive estimation (ARE) algorithm is successfully applied to electroencephalography (EEG) brain data for detecting diseases. The ARE algorithm is employed based on state-space modelling, which applies the expectation-maximization algorithm and Kalman filter. This manuscript introduces its application, for the first time, to stock-market data. For this purpose, a time-evolving interaction surface is constructed to observe the change in the surface topology. This enables tracking of the stock market’s behavior over time and differentiates between different states. This provides a deep understanding of the underlying system behavior before, during, and after a crisis. Different patterns of the stock-market movements are recognized, providing novel information regarding detecting an early-warning sign. Secondly, a Granger-causality time-domain technique, called directed partial correlation, is employed to infer the underlying interconnectivity structure among markets. This information is crucial for investors and market players, enabling them to differentiate between those markets which will fall in a catastrophic loss, and those which will not. Consequently, they can make successful decisions towards selecting less risky portfolios, which guarantees lower losses. The results showed the effectiveness of the use of this methodology in the framework of the process of early-warning detection. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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12 pages, 277 KiB

Open AccessFeature PaperArticle

On the Low Degree of Entropy Implied by the Solutions of Modern Macroeconomic Models

by Ragnar Nymoen

Entropy 2022, 24(12), 1728; https://doi.org/10.3390/e24121728 - 25 Nov 2022

Viewed by 1184

Abstract

The non-causal (“forward-looking”) solution used routinely in academic macroeconomics may represent a violation of a law of entropy, namely that the direction of time is one way (from the past and towards the present), and that the variance of economic processes increases with time. In order to re-establish a degree of compatibility with the law of entropy, so called hybrid forms are required add-ins to DSGE (Dynamic Stochastic General Equilibrium) models. However, the solution that uses hybrid forms is a particular special case of a causal solutions of autoregressive distributed lags, VARs and recursive and simultaneous equations models well known from empirical macro econometrics. Hence, hybrid forms of small scale DSGE models can be analysed and tested against competing model equations, using an econometric encompassing framework. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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10 pages, 703 KiB

Open AccessArticle

Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic

by Semei Coronado, Jose N. Martinez, Victor Gualajara and Omar Rojas

Entropy 2022, 24(10), 1420; https://doi.org/10.3390/e24101420 - 5 Oct 2022

Cited by 3 | Viewed by 1931

Abstract

The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets’ volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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30 pages, 5392 KiB

Open AccessArticle

The Causality and Uncertainty of the COVID-19 Pandemic to Bursa Malaysia Financial Services Index’s Constituents

by Daeng Ahmad Zuhri Zuhud, Muhammad Hasannudin Musa, Munira Ismail, Hafizah Bahaludin and Fatimah Abdul Razak

Entropy 2022, 24(8), 1100; https://doi.org/10.3390/e24081100 - 10 Aug 2022

Cited by 10 | Viewed by 3376

Abstract

Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index’s constituents comprise several of the strongest performing financial constituents in Bursa Malaysia’s Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent’s causal influence or intensity relative to each other’s performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent’s causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia’s sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index’s largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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17 pages, 1558 KiB

Open AccessArticle

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods

by Román A. Mendoza-Urdiales, José Antonio Núñez-Mora, Roberto J. Santillán-Salgado and Humberto Valencia-Herrera

Entropy 2022, 24(7), 874; https://doi.org/10.3390/e24070874 - 25 Jun 2022

Cited by 16 | Viewed by 8824

Abstract

Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes’ movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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18 pages, 659 KiB

Open AccessFeature PaperArticle

Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis

by Håkon Otneim, Geir Drage Berentsen and Dag Tjøstheim

Entropy 2022, 24(3), 378; https://doi.org/10.3390/e24030378 - 8 Mar 2022

Cited by 2 | Viewed by 2877

Abstract

The Granger causality test is essential for detecting lead–lag relationships between time series. Traditionally, one uses a linear version of the test, essentially based on a linear time series regression, itself being based on autocorrelations and cross-correlations of the series. In the present paper, we employ a local Gaussian approach in an empirical investigation of lead–lag and causality relations. The study is carried out for monthly recorded financial indices for ten countries in Europe, North America, Asia and Australia. The local Gaussian approach makes it possible to examine lead–lag relations locally and separately in the tails and in the center of the return distributions of the series. It is shown that this results in a new and much more detailed picture of these relationships. Typically, the dependence is much stronger in the tails than in the center of the return distributions. It is shown that the ensuing nonlinear Granger causality tests may detect causality where traditional linear tests fail. Full article

(This article belongs to the Special Issue Granger Causality and Transfer Entropy for Financial Networks)

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20 pages, 3580 KiB

Open AccessArticle

Information Flow Network of International Exchange Rates and Influence of Currencies

by Hongduo Cao, Fan Lin, Ying Li and Yiming Wu

Entropy 2021, 23(12), 1696; https://doi.org/10.3390/e23121696 - 18 Dec 2021

Cited by 3 | Viewed by 3120

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 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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23 pages, 443 KiB

Open AccessArticle

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

by Angeliki Papana

Entropy 2021, 23(12), 1570; https://doi.org/10.3390/e23121570 - 25 Nov 2021

Cited by 3 | Viewed by 4499

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 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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21 pages, 6280 KiB

Open AccessArticle

The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test

by Xunfa Lu, Kai Liu, Kin Keung Lai and Hairong Cui

Entropy 2021, 23(9), 1172; https://doi.org/10.3390/e23091172 - 6 Sep 2021

Cited by 3 | Viewed by 2991

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 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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17 pages, 716 KiB

Open AccessArticle

On the Dynamics of International Real-Estate-Investment Trust-Propagation Mechanisms: Evidence from Time-Varying Return and Volatility Connectedness Measures

by Keagile Lesame, Elie Bouri, David Gabauer and Rangan Gupta

Entropy 2021, 23(8), 1048; https://doi.org/10.3390/e23081048 - 14 Aug 2021

Cited by 24 | Viewed by 5005

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 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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