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Vine copula Granger causality in mean

Author

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  • Jang, Hyuna
  • Kim, Jong-Min
  • Noh, Hohsuk

Abstract

Ever since the Granger causality test was proposed in 1969, financial market researchers have used it heavily to determine whether the past of a one-time series facilitates the future prediction of another time series. However, as many researchers have noted, the traditional Granger causality test based on the vector autoregression model has limitations in detecting nonlinear causality. To relax the parametric model assumptions of the Granger causality test, nonparametric versions have been proposed to use the advantage of detecting nonlinear Granger causality but have shown difficulty in selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests, we propose the vine copula Granger causality test in mean based on the semiparametric time-series modeling technique. The proposed test overcomes the shortcomings of parametric modeling and has a computational advantage over the nonparametric tests. Our test shows good size and power performance with various simulated data and a real data.

Suggested Citation

  • Jang, Hyuna & Kim, Jong-Min & Noh, Hohsuk, 2022. "Vine copula Granger causality in mean," Economic Modelling, Elsevier, vol. 109(C).
    • Handle: RePEc:eee:ecmode:v:109:y:2022:i:c:s026499932200044x
    DOI: 10.1016/j.econmod.2022.105798
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    References listed on IDEAS

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    1. Ji, Hao & Wang, Hao & Zhong, Rui & Li, Min, 2020. "China's liberalizing stock market, crude oil, and safe-haven assets: A linkage study based on a novel multivariate wavelet-vine copula approach," Economic Modelling, Elsevier, vol. 93(C), pages 187-204.
    2. Kim, Jong-Min & Lee, Namgil & Hwang, Sun Young, 2020. "A Copula Nonlinear Granger Causality," Economic Modelling, Elsevier, vol. 88(C), pages 420-430.
    3. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    4. Xiaojun Song & Abderrahim Taamouti, 2018. "Measuring Nonlinear Granger Causality in Mean," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 321-333, April.
    5. Rezitis, Anthony N. & Rokopanos, Andreas & Tsionas, Mike G., 2021. "Investigating dynamic price co-movements in the international milk market using copulas: The role of trade agreements," Economic Modelling, Elsevier, vol. 95(C), pages 215-227.
    6. Xiaojun Song & Abderrahim Taamouti, 2021. "Measuring Granger Causality in Quantiles," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 937-952, October.
    7. Brendan K. Beare & Juwon Seo, 2015. "Vine Copula Specifications for Stationary Multivariate Markov Chains," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 228-246, March.
    8. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    9. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    10. Fernandes, Mário Correia & Dias, José Carlos & Nunes, João Pedro Vidal, 2021. "Modeling energy prices under energy transition: A novel stochastic-copula approach," Economic Modelling, Elsevier, vol. 105(C).
    11. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    12. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    13. Wang, Haiying & Yuan, Ying & Li, Yiou & Wang, Xunhong, 2021. "Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory," Economic Modelling, Elsevier, vol. 94(C), pages 401-414.
    14. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    15. Efstathios Paparoditis & Dimitris Politis, 2000. "The Local Bootstrap for Kernel Estimators under General Dependence Conditions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(1), pages 139-159, March.
    16. Eike Christian Brechmann & Claudia Czado, 2015. "COPAR—multivariate time series modeling using the copula autoregressive model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(4), pages 495-514, July.
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    Cited by:

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    2. Joshua Eklund & Jong-Min Kim, 2022. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression," Forecasting, MDPI, vol. 4(3), pages 1-14, July.

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    More about this item

    Keywords

    Granger causality; Multivariate time series; Semiparametric modeling; Stationary vine copula models;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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