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TESTING GARCH-X TYPE MODELS

Published online by Cambridge University Press:  18 October 2018

Rasmus Søndergaard Pedersen*
Affiliation:
University of Copenhagen
Anders Rahbek
Affiliation:
University of Copenhagen
*
*Address correspondence to Rasmus Søndergaard Pedersen, Department of Economics, University of Copenhagen, Øster Farimagsgade 5, 1353 Copenhagen K, Denmark; e-mail: rsp@econ.ku.dk.

Abstract

We present novel theory for testing for reduction of GARCH-X type models with an exogenous (X) covariate to standard GARCH type models. To deal with the problems of potential nuisance parameters on the boundary of the parameter space as well as lack of identification under the null, we exploit a noticeable property of specific zero-entries in the inverse information of the GARCH-X type models. Specifically, we consider sequential testing based on two likelihood ratio tests and as demonstrated the structure of the inverse information implies that the proposed test neither depends on whether the nuisance parameters lie on the boundary of the parameter space, nor on lack of identification. Asymptotic theory is derived essentially under stationarity and ergodicity, coupled with a regularity assumption on the exogenous covariate X. Our general results on GARCH-X type models are applied to Gaussian based GARCH-X models, GARCH-X models with Student’s t-distributed innovations as well as integer-valued GARCH-X (PAR-X) models.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

Department of Economics, University of Copenhagen, Denmark. This research was supported by the Danish Council for Independent Research (DSF Grant 015-00028B). Pedersen is grateful for support from the Carlsberg Foundation. We thank the Co-Editor (Dennis Kristensen) and two referees as well as seminar participants at Cambridge, Helsinki, and Oxford Universities. We also thank participants at (EC)2 2017, Heikametrics 2017, Toulouse Financial Econometrics Conference 2018, and Brunel Conference 2018 for comments and discussions of a previous draft of the article.

References

REFERENCES

Agosto, A., Cavaliere, G., Kristensen, D., & Rahbek, A. (2016) Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX). Journal of Empirical Finance 38, 640663.CrossRefGoogle Scholar
Ahmad, A. & Francq, C. (2016) Poisson QMLE of count time series models. Journal of Time Series Analysis 37, 291314.CrossRefGoogle Scholar
Andrews, D.W.K. (1999) Estimation when a parameter is on a boundary. Econometrica 67, 13411383.CrossRefGoogle Scholar
Andrews, D.W.K. (2001) Testing when a parameter is on the boundary of the maintained hypothesis. Econometrica 69, 683734.CrossRefGoogle Scholar
Andrews, D.W.K. & Cheng, X. (2012) Estimation and inference with weak, semi-strong, and strong identification. Econometrica 80, 21532211.Google Scholar
Berkes, I. & Horváth, L. (2004) The efficiency of the estimators of the parameters in GARCH processes. Annals of Statistics 32, 633655.Google Scholar
Bierens, H.J. & Ploberger, W. (1997) Asymptotic theory of integrated conditional moment tests. Econometrica 65, 11291151.CrossRefGoogle Scholar
Brown, B.M. (1971) Martingale central limit theorems. The Annals of Mathematical Statistics 42, 5966.CrossRefGoogle Scholar
Chernoff, A. (1954) On the distribution of the likelihood ratio. The Annals of Mathematical Statistics 25, 573578.CrossRefGoogle Scholar
Demos, A. & Sentana, E. (1998) Testing for GARCH effects: A one-sided approach. Journal of Econometrics 86, 97127.CrossRefGoogle Scholar
Fokianos, K., Rahbek, A., & Tjostheim, D. (2009) Poisson autoregression. Journal of the American Statistical Association 104, 14301439.CrossRefGoogle Scholar
Francq, C. & Thieu, L.Q. (2018) QML inference for volatility models with covariates. Econometric Theory, first published online 01 February 2018.Google Scholar
Francq, C. & Zakoïan, J.M. (2007) Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero. Stochastic Processes and Their Applications 117, 12651284.CrossRefGoogle Scholar
Francq, C. & Zakoïan, J.M. (2009) Testing the nullity of GARCH coefficients: Correction of the standard tests and relative efficiency comparisons. Journal of the American Statistical Association 104, 313324.CrossRefGoogle Scholar
Francq, C. & Zakoïan, J.M. (2010) GARCH Models: Structure, Statistical Inference and Financial Applications, Wiley.CrossRefGoogle Scholar
Han, H. (2015) Asymptotic properties of GARCH-X processes. Journal of Financial Econometrics 13, 188221.CrossRefGoogle Scholar
Han, H. & Kristensen, D. (2014) Asymptotic for the QMLE in GARCH-X models with stationary and nonstationary covariates. Journal of Business & Economic Statistics 32, 416429.CrossRefGoogle Scholar
Han, H. & Park, J.Y. (2012) ARCH/GARCH with persistent covariate: Asymptotic theory of MLE. Journal of Econometrics 167, 95112.CrossRefGoogle Scholar
Jensen, S.T. & Rahbek, A. (2004a) Asymptotic inference for nonstationary GARCH. Econometric Theory 20, 12031226.CrossRefGoogle Scholar
Jensen, S.T. & Rahbek, A. (2004b) Asymptotic normality of the QMLE estimator of ARCH in the nonstationary case. Econometrica 72, 641646.CrossRefGoogle Scholar
Ketz, P. (2018) Subvector inference when the true parameter vector may be near or at the boundary. Journal of Econometrics, first published online 05 September 2018. doi: 10.1016/j.jeconom.2018.08.003.CrossRefGoogle Scholar
Leeb, H. & Pötscher, B.M. (2005) Model selection and inference: Facts and fiction. Econometric Theory 21, 2159.CrossRefGoogle Scholar
McCloskey, A. (2017) Bonferroni-based size-correction for nonstandard testing problems. Journal of Econometrics 200, 1735.CrossRefGoogle Scholar
Mikosch, T. & Starica, C. (2000) Limit theory for the sample autocorrelations and extremes of a GARCH(1,1) process. The Annals of Statistics 28, 14271451.Google Scholar
Pedersen, R.S. (2017) Inference and testing on the boundary in extended constant conditional correlation GARCH models. Journal of Econometrics 196, 2336.CrossRefGoogle Scholar
Pedersen, R.S. & Rahbek, A. (2016) Nonstationary GARCH with 𝑡-distributed innovations. Economics Letters 138, 1921.CrossRefGoogle Scholar
Ranga Rao, R. (1962) Relations between weak and uniform convergence of measures with applications. The Annals of Mathematical Statistics 33, 659680.Google Scholar
Silvapulle, M.J. & Silvapulle, P. (1995) A score test against one-sided alternatives. Journal of the American Statistical Association 90, 342349.CrossRefGoogle Scholar
Straumann, D. (2005) Estimation in conditionally heteroscedastic time series models. Lecture Notes in Statistics. Springer.Google Scholar
White, H. (2001) Asymptotic Theory for Econometricians: Revised Edition. Emerald Group Publishing Ltd.Google Scholar