Asymptotics of the QMLE for General ARCH(q) Models

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Standard

Asymptotics of the QMLE for General ARCH(q) Models. / Kristensen, Dennis; Rahbek, Anders Christian.

I: Journal of Time Series Econometrics, Bind 1, Nr. 1, 2009, s. 1-36.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Kristensen, D & Rahbek, AC 2009, 'Asymptotics of the QMLE for General ARCH(q) Models', Journal of Time Series Econometrics, bind 1, nr. 1, s. 1-36. https://doi.org/10.2202/1941-1928.1001

APA

Kristensen, D., & Rahbek, A. C. (2009). Asymptotics of the QMLE for General ARCH(q) Models. Journal of Time Series Econometrics, 1(1), 1-36. https://doi.org/10.2202/1941-1928.1001

Vancouver

Kristensen D, Rahbek AC. Asymptotics of the QMLE for General ARCH(q) Models. Journal of Time Series Econometrics. 2009;1(1):1-36. https://doi.org/10.2202/1941-1928.1001

Author

Kristensen, Dennis ; Rahbek, Anders Christian. / Asymptotics of the QMLE for General ARCH(q) Models. I: Journal of Time Series Econometrics. 2009 ; Bind 1, Nr. 1. s. 1-36.

Bibtex

@article{6acbd380205711de9f0a000ea68e967b,
title = "Asymptotics of the QMLE for General ARCH(q) Models",
abstract = "Asymptotics of the QMLE for Non-Linear ARCH ModelsDennis Kristensen, Columbia UniversityAnders Rahbek, University of CopenhagenAbstractAsymptotic properties of the quasi-maximum likelihood estimator (QMLE) for non-linear ARCH(q) models -- including for example Asymmetric Power ARCH and log-ARCH -- are derived. Strong consistency is established under the assumptions that the ARCH process is geometrically ergodic, the conditional variance function has a finite log-moment, and finite second moment of the rescaled error. Asymptotic normality of the estimator is established under the additional assumption that certain ratios involving the conditional variance function are suitably bounded, and that the rescaled errors have little more than fourth moment. We verify our general conditions, including identification, for a wide range of leading specific ARCH models.",
author = "Dennis Kristensen and Rahbek, {Anders Christian}",
year = "2009",
doi = "10.2202/1941-1928.1001",
language = "English",
volume = "1",
pages = "1--36",
journal = "Journal of Time Series Econometrics",
issn = "2194-6507",
publisher = "De Gruyter",
number = "1",

}

RIS

TY - JOUR

T1 - Asymptotics of the QMLE for General ARCH(q) Models

AU - Kristensen, Dennis

AU - Rahbek, Anders Christian

PY - 2009

Y1 - 2009

N2 - Asymptotics of the QMLE for Non-Linear ARCH ModelsDennis Kristensen, Columbia UniversityAnders Rahbek, University of CopenhagenAbstractAsymptotic properties of the quasi-maximum likelihood estimator (QMLE) for non-linear ARCH(q) models -- including for example Asymmetric Power ARCH and log-ARCH -- are derived. Strong consistency is established under the assumptions that the ARCH process is geometrically ergodic, the conditional variance function has a finite log-moment, and finite second moment of the rescaled error. Asymptotic normality of the estimator is established under the additional assumption that certain ratios involving the conditional variance function are suitably bounded, and that the rescaled errors have little more than fourth moment. We verify our general conditions, including identification, for a wide range of leading specific ARCH models.

AB - Asymptotics of the QMLE for Non-Linear ARCH ModelsDennis Kristensen, Columbia UniversityAnders Rahbek, University of CopenhagenAbstractAsymptotic properties of the quasi-maximum likelihood estimator (QMLE) for non-linear ARCH(q) models -- including for example Asymmetric Power ARCH and log-ARCH -- are derived. Strong consistency is established under the assumptions that the ARCH process is geometrically ergodic, the conditional variance function has a finite log-moment, and finite second moment of the rescaled error. Asymptotic normality of the estimator is established under the additional assumption that certain ratios involving the conditional variance function are suitably bounded, and that the rescaled errors have little more than fourth moment. We verify our general conditions, including identification, for a wide range of leading specific ARCH models.

U2 - 10.2202/1941-1928.1001

DO - 10.2202/1941-1928.1001

M3 - Journal article

VL - 1

SP - 1

EP - 36

JO - Journal of Time Series Econometrics

JF - Journal of Time Series Econometrics

SN - 2194-6507

IS - 1

ER -

ID: 11712893