Article
Operations Research & Management Science
Salma Mefteh-Wali, Hassen Rais, Guillaume Schier
Summary: This paper extends the research on the impact of corporate social responsibility (CSR) on firm risk and discusses the integration of CSR as insurance in global risk management strategy. The empirical analysis on European-listed firms shows a directional causality effect between CSR and idiosyncratic risk, and successful modeling of the dependence structure between them.
ANNALS OF OPERATIONS RESEARCH
(2022)
Review
Mathematics, Applied
Tom Edinburgh, Stephen J. Eglen, Ari Ercole
Summary: Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is crucial in various fields such as clinical medicine, mathematical biology, economics, and environmental research. Evaluation of ten prominent causality indices showed strong agreement between methods in general, but they may not always be robust to real-world relevant transformations.
Review
Biology
Alex Eric Yuan, Wenying Shou
Summary: This article provides a critical review of three statistical causal discovery methods and their applications in ecological processes. The review examines what each method tests for, the causal statements it implies, and the potential for misinterpretation. The authors introduce new visualization techniques and highlight the limitations of so-called "model-free" causality tests. The goal of the review is to encourage thoughtful application of these methods, facilitate interdisciplinary communication, and promote explicit assumptions.
Article
Energy & Fuels
Katarzyna Kuziak, Joanna Gorka
Summary: This study examines the impact of crude oil and natural gas future returns on energy stock portfolios, using Granger causality, Dynamic Conditional Correlation, and the tail dependence-focused copula approach. The findings confirm the existence of hedging or diversification opportunities between energy ETFs and crude oil/natural gas risk factors. The results indicate that crude oil has a moderate effect on energy ETFs, while natural gas has a weaker impact, suggesting limited hedging opportunities but potential for diversification.
Article
Chemistry, Multidisciplinary
Axel Faes, Iris Vantieghem, Marc M. Van Hulle
Summary: Directed connectivity between brain sources identified from scalp EEG can provide insights into information flows in the brain and serve as a biomarker for neurological disorders. However, the correct interpretation of connectivity is challenging due to volume conductance. We investigated different neural network architectures and found that a Long Short-Term Memory network with Non-Uniform Embedding showed promising results and can compete with established methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Economics
Hyuna Jang, Jong-Min Kim, Hohsuk Noh
Summary: In this article, a vine copula Granger causality test is proposed based on the semi-parametric time-series modeling technique. This test overcomes the limitations of traditional methods and has a computational advantage.
ECONOMIC MODELLING
(2022)
Article
Multidisciplinary Sciences
Manuel Castro, Pedro Ribeiro Mendes Junior, Aurea Soriano-Vargas, Rafael de Oliveira Werneck, Maiara Moreira Goncalves, Leopoldo Lusquino Filho, Renato Moura, Marcelo Zampieri, Oscar Linares, Vitor Ferreira, Alexandre Ferreira, Alessandra Davolio, Denis Schiozer, Anderson Rocha
Summary: In this study, we propose using ensemble models (such as Random Forest) to assess the importance of input features in machine learning models, in order to establish causal relationships between variables. By analyzing oil field production data, we find that our results align with confirmed tracer information, demonstrating the effectiveness of our proposed methodology.
SCIENTIFIC REPORTS
(2023)
Article
Geosciences, Multidisciplinary
Filipi N. Silva, Didier A. Vega-Oliveros, Xiaoran Yan, Alessandro Flammini, Filippo Menczer, Filippo Radicchi, Ben Kravitz, Santo Fortunato
Summary: This study introduces a novel method using Granger causality to study climate system teleconnections, which can recover known seasonal precipitation responses and identify candidates for unexplored teleconnection responses.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Physics, Multidisciplinary
Rongbao Gu, Shengnan Liu
Summary: In this paper, the chaotic behavior and multi-fractal features of economic policy uncertainty (EPU) in China, the US, and the global are investigated. The nonlinear correlations and interactions of the three EPU indices are analyzed. The results show that the EPU indices in China, the US, and the global exhibit nonlinear characteristics of chaos and multifractality.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Business, Finance
David Y. Aharon, Ender Demir, Chi Keung Marco Lau, Adam Zaremba
Summary: The study reveals a strong causal relationship between uncertainty expressed on social media and cryptocurrency returns, especially for Bitcoin and in the tail of return distributions. These findings shed new light on the importance of cryptocurrencies as an alternative asset class in the face of global uncertainty.
RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE
(2022)
Article
Environmental Sciences
Maryam Zavareh, Viviana Maggioni, Vadim Sokolov
Summary: This study investigates the inter-relationships among stream water quality indicators, hydroclimatic variables, and land characteristics using statistical tools. Results show that watershed size, land use, and hydrometeorological variables have complex impacts on water quality.
Article
Meteorology & Atmospheric Sciences
Rolf Larsson, Erik K. Persson
Summary: This paper builds on the previous study by Kang and Larsson (2014) on the link between temperature and carbon dioxide levels by adding methane to the causality analysis. The study uses data extracted from ice cores in Dome C, Antarctica, spanning 800,000 years. Linear interpolation is employed to make the three data sets equidistant for statistical analysis. Multivariate Granger causality tests show strong evidence of bidirectional causality between temperature, carbon dioxide, and methane, confirming the findings of Kang and Larsson and establishing a trivariate feedback system.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Environmental Studies
Alexandre R. Scarcioffolo, Xiaoli Etienne
Summary: The study finds positive and significant spillover effects from crude oil to natural gas during bearish market conditions, which have weakened in recent years. Additionally, there is a bi-directional causality at different market conditions for natural gas and electricity returns, especially at moderate and high return quantiles. In recent years, the two markets have become more correlated during periods with low returns due to the transition of natural gas power plants.
Article
Computer Science, Interdisciplinary Applications
Maciej Rosol, Marcel Mlynczak, Gerard Cybulski
Summary: This paper presents a nonlinear method for causality analysis and a Python package created for this purpose. The results show that the proposed method is superior to the compared methods in detecting nonlinear causality and making accurate predictions, without indicating false causality. The created package allows for easy usage of neural networks to study the causal relationship between signals and provides the ability to analyze changes in causality over time.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Hardware & Architecture
Tianheng Zhang, Miaomiao Guo, Lei Wang, Mengfan Li
Summary: This study investigates the impact of VR use on brain fatigue by analyzing EEG signals of 16 healthy subjects. The results show significant differences in the frequency domain of brain networks between watching VR videos and traditional plane videos, suggesting higher brain fatigue in subjects watching VR videos.
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.