Inference and testing on the boundary in extended constant conditional correlation GARCH models

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Inference and testing on the boundary in extended constant conditional correlation GARCH models. / Pedersen, Rasmus Søndergaard.

In: Journal of Econometrics, Vol. 196, No. 1, 01.01.2017, p. 25-36.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Pedersen, RS 2017, 'Inference and testing on the boundary in extended constant conditional correlation GARCH models', Journal of Econometrics, vol. 196, no. 1, pp. 25-36. https://doi.org/10.1016/j.jeconom.2016.09.004

APA

Pedersen, R. S. (2017). Inference and testing on the boundary in extended constant conditional correlation GARCH models. Journal of Econometrics, 196(1), 25-36. https://doi.org/10.1016/j.jeconom.2016.09.004

Vancouver

Pedersen RS. Inference and testing on the boundary in extended constant conditional correlation GARCH models. Journal of Econometrics. 2017 Jan 1;196(1):25-36. https://doi.org/10.1016/j.jeconom.2016.09.004

Author

Pedersen, Rasmus Søndergaard. / Inference and testing on the boundary in extended constant conditional correlation GARCH models. In: Journal of Econometrics. 2017 ; Vol. 196, No. 1. pp. 25-36.

Bibtex

@article{0406c9947c43416fb6fac7a8b2fffe8b,
title = "Inference and testing on the boundary in extended constant conditional correlation GARCH models",
abstract = "We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and score statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.",
keywords = "Boundary, ECCC-GARCH, QML, Spillovers, C32, C51, C58",
author = "Pedersen, {Rasmus S{\o}ndergaard}",
year = "2017",
month = "1",
day = "1",
doi = "10.1016/j.jeconom.2016.09.004",
language = "English",
volume = "196",
pages = "25--36",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Inference and testing on the boundary in extended constant conditional correlation GARCH models

AU - Pedersen, Rasmus Søndergaard

PY - 2017/1/1

Y1 - 2017/1/1

N2 - We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and score statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.

AB - We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and score statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.

KW - Boundary

KW - ECCC-GARCH

KW - QML

KW - Spillovers

KW - C32

KW - C51

KW - C58

U2 - 10.1016/j.jeconom.2016.09.004

DO - 10.1016/j.jeconom.2016.09.004

M3 - Journal article

AN - SCOPUS:84992481921

VL - 196

SP - 25

EP - 36

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -

ID: 186156997