Multivariate Causality Tests with Simulation and Application

30 Pages Posted: 18 Jun 2010 Last revised: 20 Jun 2010

See all articles by Zhidong Bai

Zhidong Bai

Northeast Normal University

Bingzhi Zhang

Columbia University-Department of BioStatistics

Heng Li

Hong Kong Baptist University (HKBU) - Department of Mathematics

Wing-Keung Wong

Asia University, Department of Finance

Date Written: June 18, 2010

Abstract

The traditional linear Granger causality test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a nonlinear causality test in multivariate settings. 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 to analyze the relationships among different Chinese stock market indices.

Keywords: linear Granger Causality, Nonlinear Granger Causality, U-Statistics, Simulation, Stock Markets

JEL Classification: C01, C12, G10

Suggested Citation

Bai, Zhidong and Zhang, Bingzhi and Li, Heng and Wong, Wing-Keung, Multivariate Causality Tests with Simulation and Application (June 18, 2010). Available at SSRN: https://ssrn.com/abstract=1626782 or http://dx.doi.org/10.2139/ssrn.1626782

Zhidong Bai

Northeast Normal University ( email )

Changchun
China

Bingzhi Zhang

Columbia University-Department of BioStatistics ( email )

3022 Broadway
New York, NY 10027
United States

Heng Li

Hong Kong Baptist University (HKBU) - Department of Mathematics ( email )

Kowloon Tong
Hong Kong
Hong Kong

Wing-Keung Wong (Contact Author)

Asia University, Department of Finance ( email )

Taiwan
Taiwan

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