Multivariate causality tests with simulation and application
This paper extends the test established by Hiemstra and Jones (1994) to develop a nonlinear causality test in a multivariate setting. A Monte Carlo simulation is conducted to demonstrate the superiority of our proposed multivariate test over its bivariate counterpart. In addition, we illustrate the applicability of our proposed test for analyzing the relationships among different Chinese stock market indices.
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Volume (Year): 81 (2011)
Issue (Month): 8 (August)
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References listed on IDEAS
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- 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 1639-64, December.
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- Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
- Qiao, Zhuo & McAleer, Michael & Wong, Wing-Keung, 2009. "Linear and nonlinear causality between changes in consumption and consumer attitudes," Economics Letters, Elsevier, vol. 102(3), pages 161-164, March.
- Qiao, Zhuo & Li, Yuming & Wong, Wing-Keung, 2008. "Policy change and lead-lag relations among China's segmented stock markets," Journal of Multinational Financial Management, Elsevier, vol. 18(3), pages 276-289, July.
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