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Journal articles on the topic 'Conditional Granger causality'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Malekpour, Sheida, and William A. Sethares. "Conditional Granger causality and partitioned Granger causality: differences and similarities." Biological Cybernetics 109, no. 6 (2015): 627–37. http://dx.doi.org/10.1007/s00422-015-0665-3.

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2

CEVHER, Erdogan. "causfinder: An R package for Systemwise Analysis of Conditional and Partial Granger Causalities." International Journal of Science and Advanced Technology 4, no. 10 (2014): 6–15. https://doi.org/10.5281/zenodo.35599.

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Multivariate Granger causality analysis is the study where at least one of the sets of independent and dependent variables includes more than 1 variable when these variables are conditioned on third set of variables in the analyzed system. causfinder reveals – via systemwise approaching the G-causalities – all conditional and partial Granger causalities in the system in any desired pattern formed by various distributions of these variables to these three sets. It also reveals the character of variables from more independent variables to dependent ones with the degrees and direction

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钟, 静静. "Reconstructing Networks via Multivariate Conditional Granger Causality." Advances in Applied Mathematics 14, no. 06 (2025): 13–22. https://doi.org/10.12677/aam.2025.146296.

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4

Lu, Xun, Liangjun Su, and Halbert White. "GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA." Econometric Theory 33, no. 2 (2016): 263–91. http://dx.doi.org/10.1017/s0266466616000086.

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Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separabilit

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YURDAKUL, Funda, and Erdogan CEVHER. "Determinants of Current Account Deficit in Turkey: The Conditional and Partial Granger Causality Approach." Procedia Economics and Finance 26, no. 2015 (2015): 92–100. https://doi.org/10.1016/S2212-5671(15)00884-9.

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This study aims to reveal the causality relations between the macro aggregates that affect current deficit using conditional and partial Granger causality test. Current deficit/GDP, growth rate, real effective exchange rate, direct foreign capital investment, openness, and energy import were selected as variables for this purpose. 2003.1-2014.2 quarterly data for Turkey’s economy were used for analysis. The results of the conditional and partial Granger causality test demonstrate that real effective exchange rate is the variable with greatest impact on current deficit

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Xiaoguang Liu, Xiaoguang Liu, Renping Song Xiaoguang Liu, Guoliang Ding Renping Song, Mingxia Zu Guoliang Ding, Xiaomei Wang Mingxia Zu, and Yuan Wang Xiaomei Wang. "A Prediction Model for Substation Investment Benefit Based on Granger Causality." 電腦學刊 33, no. 6 (2022): 107–17. http://dx.doi.org/10.53106/199115992022123306009.

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<p>The construction of a lean operation and inspection integrated management system for substations is an important part of the development and maintenance of the power system. Forecasting the investment benefits of substation project development is an important issue in feasibility analysis. Therefore, we need to use a highly accurate method to make a prediction of the investment benefit of this project. Granger causation is a causal relationship based on &quot;prediction&quot;, and inferring about its causality is a key task in time series analysis. In this paper, we propose a

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Stokes, P. A., M. Vangel, F. H. Lin, et al. "Dynamic Frequency-Domain Conditional Granger Causality Applied to Magnetoencephalography." NeuroImage 47 (July 2009): S148. http://dx.doi.org/10.1016/s1053-8119(09)71509-5.

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Xiao, Yanyang, Songting Li, and Douglas Zhou. "Representing conditional Granger causality by vector auto-regressive parameters." Communications in Mathematical Sciences 17, no. 5 (2019): 1353–86. http://dx.doi.org/10.4310/cms.2019.v17.n5.a9.

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9

Zhou, Zhenyu, Yonghong Chen, Mingzhou Ding, Paul Wright, Zuhong Lu, and Yijun Liu. "Analyzing brain networks with PCA and conditional Granger causality." Human Brain Mapping 30, no. 7 (2009): 2197–206. http://dx.doi.org/10.1002/hbm.20661.

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10

Marica, Vasile George, and Alexandra Horobet. "Conditional Granger Causality and Genetic Algorithms in VAR Model Selection." Symmetry 11, no. 8 (2019): 1004. http://dx.doi.org/10.3390/sym11081004.

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Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, constructed on two pillars: conditional Granger causality and Lasso regression. Departing from a recent information theory proof that Granger causality and transfer entropy are equivalent, we propose a heuristic method for the identification of true structural dependencies in multivariate economic time series. Through rigorous testing, both empirically and

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Mohd. Khan, Caroline. "Currency Crises and Commodity Markets: Dynamic Relationships and Implications for Sustainable Investing and International Trade." European Journal of Sustainable Development 14, no. 2 (2025): 861. https://doi.org/10.14207/ejsd.2025.v14n2p861.

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This study examines the inter-relation between WTI crude oil futures prices and two energy-related exchange-traded funds (ETFs): the ETF of iShares Global Clean Energy (Clean Energy) and the ETF of Energy Sector SPDR Fund (Traditional Energy). Using Granger causality tests and the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model, we analyze causal relationships and volatility transmission between these assets. The Granger causality results show that traditional energy markets dominate, while clean energy markets are becoming more influ

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12

Wang, Xia, and Yongmiao Hong. "CHARACTERISTIC FUNCTION BASED TESTING FOR CONDITIONAL INDEPENDENCE: A NONPARAMETRIC REGRESSION APPROACH." Econometric Theory 34, no. 4 (2017): 815–49. http://dx.doi.org/10.1017/s026646661700010x.

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We propose a characteristic function based test for conditional independence, applicable to both cross-sectional and time series data. We also derive a class of derivative tests, which deliver model-free tests for such important hypotheses as omitted variables, Granger causality in various moments and conditional uncorrelatedness. The proposed tests have a convenient asymptotic null N (0, 1) distribution, and are asymptotically locally more powerful than a variety of related smoothed nonparametric tests in the literature. Unlike other smoothed nonparametric tests for conditional independence,

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13

Seth, S., and J. C. Principe. "Assessing Granger Non-Causality Using Nonparametric Measure of Conditional Independence." IEEE Transactions on Neural Networks and Learning Systems 23, no. 1 (2012): 47–59. http://dx.doi.org/10.1109/tnnls.2011.2178327.

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14

Brovelli, Andrea. "Statistical Analysis of Single-Trial Granger Causality Spectra." Computational and Mathematical Methods in Medicine 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/697610.

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Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated

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Mainali, Kumar, Sharon Bewick, Briana Vecchio-Pagan, David Karig, and William F. Fagan. "Detecting interaction networks in the human microbiome with conditional Granger causality." PLOS Computational Biology 15, no. 5 (2019): e1007037. http://dx.doi.org/10.1371/journal.pcbi.1007037.

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16

Taamouti, Abderrahim, Taoufik Bouezmarni, and Anouar El Ghouch. "Nonparametric estimation and inference for conditional density based Granger causality measures." Journal of Econometrics 180, no. 2 (2014): 251–64. http://dx.doi.org/10.1016/j.jeconom.2014.03.001.

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17

Falasca, N. W., and R. Franciotti. "Ability of Granger Causality Analysis to Detect Indirect Links: A Simulation Study." Nonlinear Phenomena in Complex Systems 23, no. 2 (2020): 121–24. http://dx.doi.org/10.33581/1561-4085-2020-23-2-121-124.

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Granger causality (G-causality) has emerged as a useful tool to investigate the influence that one system can exert over another system, but challenges remain when applying it to biological data. Specifically, it is not clear if G-causality can distinguish between direct and indirect influences. In this study time domain G-causality connectivity analysis was performed on simulated electroencephalographic cerebral signals. Conditional multivariate autoregressive model was applied to 19 virtual time series (nodes) to identify the effects of direct and indirect links while varying one of the foll

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18

KATIRCIOǦLU, SALIH TURAN. "TOURISM AND GROWTH IN SINGAPORE: NEW EXTENSION FROM BOUNDS TEST TO LEVEL RELATIONSHIPS AND CONDITIONAL GRANGER CAUSALITY TESTS." Singapore Economic Review 56, no. 03 (2011): 441–53. http://dx.doi.org/10.1142/s0217590811004365.

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This paper empirically investigates the tourism-led growth (TLG) hypothesis in the case of Singapore by employing the bounds test to cointegration, error correction models and Granger causality tests using annual data from 1960 to 2007. Results confirm the existence of long-term equilibrium relationship between international tourism and economic growth in the case of Singapore; real income growth converges to its long-term equilibrium level significantly by 51.4% in the TLG model. The major finding of this study is that the TLG hypothesis is confirmed for the Singaporean economy in the long-te

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19

Shah, Jainam. "The Relationship Between the Volatility of the S&P 500 and CBOE Volatility Index (VIX)." International Journal of Social Science and Economic Research 09, no. 09 (2024): 3840–51. http://dx.doi.org/10.46609/ijsser.2024.v09i09.039.

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This study investigates the bidirectional Granger causation between the CBOE Volatility Index (VIX) and the volatility of the S&P 500 Index utilizing data obtained from Yahoo Finance. The GARCH (1,1) model is employed for the estimation of conditional volatility. This study employs Granger Causality Tests to determine whether the volatility of the S&P 500 can be forecasted by the VIX Index and vice versa. The results show significant bidirectional Granger causality, indicating that the VIX Index and the S&P 500's historical volatility may be accurately predicted from each other. Th

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20

Ai, Dongmei, Xiaoxin Li, Gang Liu, Xiaoyi Liang, and Li Xia. "Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality." Genes 10, no. 3 (2019): 216. http://dx.doi.org/10.3390/genes10030216.

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The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial

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21

Bouezmarni, Taoufik, Jeroen V. K. Rombouts, and Abderrahim Taamouti. "Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality." Journal of Business & Economic Statistics 30, no. 2 (2012): 275–87. http://dx.doi.org/10.1080/07350015.2011.638831.

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22

Liao, Wei, Dante Mantini, Zhiqiang Zhang, et al. "Evaluating the effective connectivity of resting state networks using conditional Granger causality." Biological Cybernetics 102, no. 1 (2009): 57–69. http://dx.doi.org/10.1007/s00422-009-0350-5.

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23

Jinlong Mei, Jinlong Mei, Kai Chen Kai Chen, Yanyang Xiao Yanyang Xiao, Songting Li Songting Li, and Douglas Zhou Douglas Zhou. "The Asymptotic Behavior of Conditional Granger Causality with Respect to Sampling Interval." CSIAM Transactions on Life Sciences 1, no. 1 (2024): 22–43. https://doi.org/10.4208/csiam-ls.so-2024-0003.

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24

Nakajima, Tadahiro. "Test for volatility spillover effects in Japan’s oil futures markets by a realized variance approach." Studies in Economics and Finance 36, no. 2 (2019): 224–39. http://dx.doi.org/10.1108/sef-01-2017-0011.

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Purpose The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. Design/methodology/approach The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these

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25

Dhifaoui, Zouhaier, and Faicel Gasmi. "Linear and nonlinear linkage of conditional stochastic volatility of interbank interest rates: Empirical evidence of the BRICS countries." BRICS Journal of Economics 2, no. 2 (2021): 4–16. http://dx.doi.org/10.38050/2712-7508-2021-2-1.

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The purpose of this article is to detect a possible linear and nonlinear causal relationship between the conditional stochastic volatility of log return of interbank interest rates for the BRICS countries in the period from January 2015 to October 2018. To extract the volatility of the analyzed time series, we use a stochastic volatility model with moving average innovations. To test a causal relationship between the estimated stochastic volatilities, two steps are applied. First, we used the Granger causality test and a vector autoregressive model (VAR). Secondly, we applied the nonlinear Gra

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26

Ballester, Laura, Ana Mónica Escrivá, and Ana González-Urteaga. "The Nexus between Sovereign CDS and Stock Market Volatility: New Evidence." Mathematics 9, no. 11 (2021): 1201. http://dx.doi.org/10.3390/math9111201.

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This paper extends the studies published to date by performing an analysis of the causal relationships between sovereign CDS spreads and the estimated conditional volatility of stock indices. This estimation is performed using a vector autoregressive model (VAR) and dynamically applying the Granger causality test. The conditional volatility of the stock market has been obtained through various univariate GARCH models. This methodology allows us to study the information transmissions, both unidirectional and bidirectional, that occur between CDS spreads and stock volatility between 2004 and 202

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27

Lopez-Doriga, B., M. Atzori, R. Vinuesa, H. J. Bae, A. Srivastava, and S. T. M. Dawson. "Linear and nonlinear Granger causality analysis of turbulent duct flows." Journal of Physics: Conference Series 2753, no. 1 (2024): 012017. http://dx.doi.org/10.1088/1742-6596/2753/1/012017.

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Abstract This research focuses on the identification and causality analysis of coherent structures that arise in turbulent flows in square and rectangular ducts. Coherent structures are first identified from direct numerical simulation data via proper orthogonal decomposition (POD), both by using all velocity components, and after separating the streamwise and secondary components of the flow. The causal relations between the mode coefficients are analysed using pairwise-conditional Granger causality analysis. We also formulate a nonlinear Granger causality analysis that can account for nonlin

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28

Xi, Zhuo. "The Asymmetric Volatility Spillover and Dynamic Correlation Across Equity Markets in China and The United States." International Journal of Business & Management Studies 04, no. 06 (2023): 31–43. http://dx.doi.org/10.56734/ijbms.v4n6a5.

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This paper aims to study the volatility spillover effects as well as the dynamic conditional correlation between stock market returns in China and the U.S. Firstly, the analysis uses a vector autoregression with a bivariate BEKK-GARCH model to capture the asymmetric volatility transmissions between the two markets during the sample of 1996-2019. Then a VAR-DCC-GARCH model is employed to estimate the dynamic conditional correlation between these two market returns. Finally, linear regression and Granger Causality test are conducted to further explore the effect of the U.S policy rates on such c

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Jiang, Wei, Jingang Jiang, and Sonia Chien-I. Chen. "Untangling Carbon–Clean Energy Dynamics: A Quantile Granger-Causality Perspective." Sustainability 17, no. 7 (2025): 3118. https://doi.org/10.3390/su17073118.

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This study examines the dynamic relationship between carbon markets and clean energy stocks using a quantile Granger-causality framework, capturing nonlinear dependencies across different market conditions. Unlike conventional mean-based approaches, this method identifies asymmetric causal linkages, particularly during periods of extreme market movements. Empirical results reveal a bidirectional Granger-causal relationship between carbon price returns and clean energy stock returns, predominantly at the lower quantile τ=0.25 and upper quantile τ=0.75 of the conditional distribution. This indic

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Shao, Yuhao, Daniel Fiifi Tawia Hagan, Shijie Li, Feihong Zhou, Xiao Zou, and Pedro Cabral. "The Many Shades of the Vegetation–Climate Causality: A Multimodel Causal Appreciation." Forests 15, no. 8 (2024): 1430. http://dx.doi.org/10.3390/f15081430.

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The causal relationship between vegetation and temperature serves as a driving factor for global warming in the climate system. However, causal relationships are typically characterized by complex facets, particularly within natural systems, necessitating the ongoing development of robust approaches capable of addressing the challenges inherent in causality analysis. Various causality approaches offer distinct perspectives on understanding causal structures, even when experiments are meticulously designed with a specific target. Here, we use the complex vegetation–climate interaction to demons

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31

Seghouane, Abd-Krim, and Shun-ichi Amari. "Identification of Directed Influence: Granger Causality, Kullback-Leibler Divergence, and Complexity." Neural Computation 24, no. 7 (2012): 1722–39. http://dx.doi.org/10.1162/neco_a_00291.

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Detecting and characterizing causal interdependencies and couplings between different activated brain areas from functional neuroimage time series measurements of their activity constitutes a significant step toward understanding the process of brain functions. In this letter, we make the simple point that all current statistics used to make inferences about directed influences in functional neuroimage time series are variants of the same underlying quantity. This includes directed transfer entropy, transinformation, Kullback-Leibler formulations, conditional mutual information, and Granger ca

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32

Maneejuk, Paravee, and Woraphon Yamaka. "The Role of Economic Contagion in the Inward Investment of Emerging Economies: The Dynamic Conditional Copula Approach." Mathematics 9, no. 20 (2021): 2540. http://dx.doi.org/10.3390/math9202540.

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Contagion has been one of the most widely studied and challenging problems in recent economic research. This paper aims at capturing the main impact of contagion risk of the U.S. on foreign direct investment inflows in 18 emerging countries. To quantify the degree of contagion, the time-varying tail dependence copula is employed. Then, the Granger causality test and time series regression analysis are used to investigate the temporal and contemporaneous effects of contagion risk on investment inflows, respectively. Overall, the results confirm the time-varying contagion effects of the U.S. eco

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33

WONG, HOCK TSEN. "REAL EXCHANGE RATE RETURNS AND REAL STOCK PRICE RETURNS IN THE STOCK MARKET OF MALAYSIA." Singapore Economic Review 64, no. 05 (2016): 1319–49. http://dx.doi.org/10.1142/s0217590816500387.

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This study examines the relationships between real exchange rate returns and real stock price returns in the stock market of Malaysia. The Kwiatkowski, Phillips, Schmidt and Shin (KPSS) and Dickey and Fuller (DF) unit root test statistics show that all the variables examined are found to be stationary in the first differences. The constant conditional correlation (CCC)-multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model shows that real exchange rate return of Malaysian ringgit against the United States dollar (RM/USD) and real stock price return of Kuala Lumpu

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34

Mubarok, Faizul. "Volatility of Islamic stock market and exchange rate: Granger causality and GARCH Approach." Journal of Innovation in Business and Economics 7, no. 01 (2023): 29–38. http://dx.doi.org/10.22219/jibe.v7i01.23473.

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This research aims to investigate the causal relationship between the Islamic stock market and the exchange rate, as well as examine the volatility of the Islamic stock index in emerging countries. The study utilized the Granger causality test to analyze the causality between the Islamic stock market and the exchange rate and employed the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for volatility analysis and forecasting. For this research, daily time series data ranging from 2012 to 2022 from the Islamic stock indices of Malaysia, Turkey, India, Pakistan, Indonesia

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35

Lawal, Gold Olamide, Bisola Aladenika, Seyi Saint Akadiri, Ayodeji Samson Fatigun, and Victoria Olushola Olanrewaju. "Geopolitical Risk, Globalization and Environmental Degradation in South Africa: Evidence from Advanced Quantiles Approach." Problemy Ekorozwoju 18, no. 1 (2023): 207–15. http://dx.doi.org/10.35784/pe.2023.1.22.

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Sustainable development involves the incorporation of socio-economic concerns and environmental protection into the economic decision-making process, in such a way that, any developmental effort would eventually be favorable to immediate and future generations. It is against this backdrop this study investigates the effects of geopolitical risk and globalization on environmental degradation in South Africa over the period 1985Q1-2018Q4. This study improves on existing studies and raises concerns on the potential twin-effect of geopolitical risk and globalization on the environment. We deviate

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36

Yurdakul, Funda, and Erdogan Cevher. "Determinants of Current Account Deficit in Turkey: The Conditional and Partial Granger Causality Approach." Procedia Economics and Finance 26 (2015): 92–100. http://dx.doi.org/10.1016/s2212-5671(15)00884-9.

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Chen, Yonghong, Steven L. Bressler, and Mingzhou Ding. "Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data." Journal of Neuroscience Methods 150, no. 2 (2006): 228–37. http://dx.doi.org/10.1016/j.jneumeth.2005.06.011.

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Zhou, Zhenyu, Xunheng Wang, Nelson J. Klahr, et al. "A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging." Magnetic Resonance Imaging 29, no. 3 (2011): 418–33. http://dx.doi.org/10.1016/j.mri.2010.10.008.

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Корнилов, М. В., та И. В. Сысоев. "Реконструкция архитектуры связей в цепочке из трех однонаправленно связанных систем методом причинности по Грейнджеру". Письма в журнал технической физики 44, № 10 (2018): 86. http://dx.doi.org/10.21883/pjtf.2018.10.46103.17201.

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AbstractWe propose a method for reconstructing links in a chain of unidirectionally coupled systems by means of three tests for estimating direct and mediated coupling with the aid of Granger conditional causality in terms of prognostic models with polynomial nonlinearity. It is shown that this approach allows the architecture of links in these systems to be correctly identified in more than 80% of cases.

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Nyati, Malibongwe Cyprian, and Simiso Msomi. "Macroprudential and monetary policies in South Africa: complements or substitutes?" International Journal of Research in Business and Social Science (2147- 4478) 14, no. 4 (2025): 211–28. https://doi.org/10.20525/ijrbs.v14i4.4090.

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The article reports on the complementarity and substitutability of Macroprudential and Monetary policies in South Africa, based on the interactions of both Business and Financial cycles. To this end, a Dynamic Conditional Correlations and the Asymmetric Dynamic Conditional Correlations MGARCH models together with the Granger Causality model, the Artificial Neural Network VAR model, and the Structural VAR model, were adopted for the analyses of synchronization, causality, and the analysis of shocks to cycles, respectively. Empirical evidence obtained is such that, under conditions of financial

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Yang, Guanxue, Shimin Lei, and Guanxiao Yang. "Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems." Entropy 26, no. 12 (2024): 1063. https://doi.org/10.3390/e26121063.

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Inferring causal networks from noisy observations is of vital importance in various fields. Due to the complexity of system modeling, the way in which universal and feasible inference algorithms are studied is a key challenge for network reconstruction. In this study, without any assumptions, we develop a novel model-free framework to uncover only the direct relationships in networked systems from observations of their nonlinear dynamics. Our proposed methods are termed multiple-order Polynomial Conditional Granger Causality (PCGC) and sparse PCGC (SPCGC). PCGC mainly adopts polynomial functio

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Vidhusha S and Kavitha Anandan. "Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 2 (2016): 95–108. http://dx.doi.org/10.4018/ijcini.2016040105.

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Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task i.e., “rest.” Adults with ASD have been found to show weaker connectivity relative to controls. This work focuses on analyzing the brain activation for autistic subjects, measured by fMRI during rest, relative to the control group using interhemispherical analysis. Though both groups activated similarly in cortical areas, indications of under connectivity were exhibited by the autistic group measured by

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Chowdhury, Abdur. "Inflation and inflation-uncertainty in India: the policy implications of the relationship." Journal of Economic Studies 41, no. 1 (2014): 71–86. http://dx.doi.org/10.1108/jes-04-2012-0046.

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Purpose – Inflation and its related uncertainty can impose costs on real economic output in any economy. This paper aims to analyze the relationship between inflation and inflation uncertainty in India. Design/methodology/approach – The methodology uses a generalized autoregressive conditional heteroscedasticity (GARCH) model and Granger Causality test. Findings – Initial estimates show the inflation rate to be a stationary process. The maximum likelihood estimates from the GARCH model reveal strong support for the presence of a positive relationship between the level of inflation and its unce

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Hasbullah, Endang Soeryana, Endang Rusyaman, and Alit Kartiwa. "THE GARCH MODEL VOLATILITY OF SHARIA STOCKS ASSOCIATED CAUSALITY WITH MARKET INDEX." International Journal of Quantitative Research and Modeling 1, no. 1 (2020): 18–28. http://dx.doi.org/10.46336/ijqrm.v1i1.3.

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The purpose of this paper is to examine the volatility of Islamic stocks related to the causality of the composite stock price index (CSPI). The aim is to investigate the causality of several levels of stock returns with the movement of the CSPI, and determine its volatility as a measure of risk. To determine the causality relationship is done by using the granger causality test method, with Vector Autoregressive (VAR) modeling. Whereas to determine the volatility is done using the Generalized Autoregressive Conditional Heteroscedastisiy (GARCH) model approach. The results of the causality tes

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Detto, Matteo, Gil Bohrer, Jennifer Nietz, et al. "Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest." Entropy 15, no. 12 (2013): 4266–84. http://dx.doi.org/10.3390/e15104266.

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Chen, Han-Sheng, Zhengbing Yan, Xuelei Zhang, Yi Liu, and Yuan Yao. "Root Cause Diagnosis of Process Faults Using Conditional Granger Causality Analysis and Maximum Spanning Tree." IFAC-PapersOnLine 51, no. 18 (2018): 381–86. http://dx.doi.org/10.1016/j.ifacol.2018.09.330.

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Di Sciorio, Fabrizio, Raffaele Mattera, and Juan Evangelista Trinidad Segovia. "Measuring conditional correlation between financial markets' inefficiency." Quantitative Finance and Economics 7, no. 3 (2023): 491–507. http://dx.doi.org/10.3934/qfe.2023025.

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<abstract><p>Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ($ h_t $). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemi

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Nath Sahu, Tarak, Kalpataru Bandopadhyay, and Debasish Mondal. "An empirical study on the dynamic relationship between oil prices and Indian stock market." Managerial Finance 40, no. 2 (2014): 200–215. http://dx.doi.org/10.1108/mf-06-2013-0131.

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Purpose – This study aims to investigate the dynamic relationships between oil price shocks and Indian stock market. Design/methodology/approach – The study used daily data for the period starting from January 2001 to March 2013. In this study, Johansen's cointegration test, vector error correction model (VECM), Granger causality test, impulse response functions (IRFs) and variance decompositions (VDCs) test have been applied to exhibit the long-run and short-run relationship between them. Findings – The cointegration result indicates the existence of long-term relationship. Further, the error

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Yamaka, Woraphon, Jianxu Liu, Mingyang Li, Paravee Maneejuk, and Hai Q. Dinh. "Analyzing the Causality and Dependence between Exchange Rate and Real Estate Prices in Boom-and-Bust Markets: Quantile Causality and DCC Copula GARCH Approaches." Axioms 11, no. 3 (2022): 113. http://dx.doi.org/10.3390/axioms11030113.

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Unlike most previous studies examining the causal relationship and dependence between exchange rates and real estate prices, this study aims to investigate the causal relationship and dependence between these two variables in a boom-and-bust market setting using the panel quantile Granger causality and dynamic conditional correlation (DCC) copula GARCH approaches, respectively. In the panel quantile Granger causality test, quantiles 0.1 and 0.9 are considered to represent extreme markets (bust and boom, respectively). Our first results showed the causal effects at extreme quantiles to be very

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Zhu, Dan, Huawei Wang, and Xianghua Tan. "Mining Delay Propagation Causality within an Airport Network from Historical Data." Aerospace 11, no. 7 (2024): 533. http://dx.doi.org/10.3390/aerospace11070533.

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Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlat

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