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Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study

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Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study. / Pavlidis, Efthymios; Paya, Ivan; Peel, David.
In: Studies in Nonlinear Dynamics and Econometrics, Vol. 17, No. 3, 02.2013, p. 297-312.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Pavlidis, E, Paya, I & Peel, D 2013, 'Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study', Studies in Nonlinear Dynamics and Econometrics, vol. 17, no. 3, pp. 297-312. https://doi.org/10.1515/snde-2012-0067

APA

Pavlidis, E., Paya, I., & Peel, D. (2013). Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study. Studies in Nonlinear Dynamics and Econometrics, 17(3), 297-312. https://doi.org/10.1515/snde-2012-0067

Vancouver

Pavlidis E, Paya I, Peel D. Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study. Studies in Nonlinear Dynamics and Econometrics. 2013 Feb;17(3):297-312. doi: 10.1515/snde-2012-0067

Author

Pavlidis, Efthymios ; Paya, Ivan ; Peel, David. / Nonlinear causality tests and multivariate conditional heteroskedasticity : a simulation study. In: Studies in Nonlinear Dynamics and Econometrics. 2013 ; Vol. 17, No. 3. pp. 297-312.

Bibtex

@article{f0a259f14ad7430bbb07f711a6118149,
title = "Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study",
abstract = "This paper assesses the performance of linear and nonlinear causality tests in the presence of multivariate conditional heteroskedasticity, exogenous volatility regressors, and additive volatility outliers. Monte Carlo simulations show that tests based on the least squares covariance matrix estimator can frequently lead to finding spurious Granger causality. The degree of oversizing tends to increase with the sample size and is substantially larger for the nonlinear test. On the other hand, heteroskedasticity-robust tests which are based on the Fixed Design Wild Bootstrap perform adequately in terms of size and power. Consequently, reliable causality in mean tests can be conducted without the need to specify a conditional variance function. As an empirical application, we re-examine the return-volume relationship.",
keywords = "causality , multivariate ARCH, robust inference, additive volatility outliers, exogenous volatility regressors, monte carlo simulations",
author = "Efthymios Pavlidis and Ivan Paya and David Peel",
year = "2013",
month = feb,
doi = "10.1515/snde-2012-0067",
language = "English",
volume = "17",
pages = "297--312",
journal = "Studies in Nonlinear Dynamics and Econometrics",
issn = "1558-3708",
publisher = "Berkeley Electronic Press",
number = "3",

}

RIS

TY - JOUR

T1 - Nonlinear causality tests and multivariate conditional heteroskedasticity

T2 - a simulation study

AU - Pavlidis, Efthymios

AU - Paya, Ivan

AU - Peel, David

PY - 2013/2

Y1 - 2013/2

N2 - This paper assesses the performance of linear and nonlinear causality tests in the presence of multivariate conditional heteroskedasticity, exogenous volatility regressors, and additive volatility outliers. Monte Carlo simulations show that tests based on the least squares covariance matrix estimator can frequently lead to finding spurious Granger causality. The degree of oversizing tends to increase with the sample size and is substantially larger for the nonlinear test. On the other hand, heteroskedasticity-robust tests which are based on the Fixed Design Wild Bootstrap perform adequately in terms of size and power. Consequently, reliable causality in mean tests can be conducted without the need to specify a conditional variance function. As an empirical application, we re-examine the return-volume relationship.

AB - This paper assesses the performance of linear and nonlinear causality tests in the presence of multivariate conditional heteroskedasticity, exogenous volatility regressors, and additive volatility outliers. Monte Carlo simulations show that tests based on the least squares covariance matrix estimator can frequently lead to finding spurious Granger causality. The degree of oversizing tends to increase with the sample size and is substantially larger for the nonlinear test. On the other hand, heteroskedasticity-robust tests which are based on the Fixed Design Wild Bootstrap perform adequately in terms of size and power. Consequently, reliable causality in mean tests can be conducted without the need to specify a conditional variance function. As an empirical application, we re-examine the return-volume relationship.

KW - causality

KW - multivariate ARCH

KW - robust inference

KW - additive volatility outliers

KW - exogenous volatility regressors

KW - monte carlo simulations

U2 - 10.1515/snde-2012-0067

DO - 10.1515/snde-2012-0067

M3 - Journal article

VL - 17

SP - 297

EP - 312

JO - Studies in Nonlinear Dynamics and Econometrics

JF - Studies in Nonlinear Dynamics and Econometrics

SN - 1558-3708

IS - 3

ER -