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An algorithm for nonparametric GARCH modelling

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An algorithm for nonparametric GARCH modelling. / Bühlmann, Peter; McNeil, Alexander J.

In: Computational Statistics & Data Analysis, Vol. 40, No. 4, 28.10.2002, p. 665-683.

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Harvard

Bühlmann, P & McNeil, AJ 2002, 'An algorithm for nonparametric GARCH modelling', Computational Statistics & Data Analysis, vol. 40, no. 4, pp. 665-683. https://doi.org/10.1016/S0167-9473(02)00080-4

APA

Bühlmann, P., & McNeil, A. J. (2002). An algorithm for nonparametric GARCH modelling. Computational Statistics & Data Analysis, 40(4), 665-683. https://doi.org/10.1016/S0167-9473(02)00080-4

Vancouver

Bühlmann P, McNeil AJ. An algorithm for nonparametric GARCH modelling. Computational Statistics & Data Analysis. 2002 Oct 28;40(4):665-683. https://doi.org/10.1016/S0167-9473(02)00080-4

Author

Bühlmann, Peter ; McNeil, Alexander J. / An algorithm for nonparametric GARCH modelling. In: Computational Statistics & Data Analysis. 2002 ; Vol. 40, No. 4. pp. 665-683.

Bibtex - Download

@article{6aeb4828fb96472a88f645b9a6a4994d,
title = "An algorithm for nonparametric GARCH modelling",
abstract = "A simple iterative algorithm for nonparametric first-order GARCH modelling is proposed. This method offers an alternative to fitting one of the many different parametric GARCH specifications that have been proposed in the literature. A theoretical justification for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical financial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH specification. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested.",
keywords = "GARCH modelling, Nonparametric methods, Volatility estimation",
author = "Peter B{\"u}hlmann and McNeil, {Alexander J.}",
year = "2002",
month = oct,
day = "28",
doi = "10.1016/S0167-9473(02)00080-4",
language = "English",
volume = "40",
pages = "665--683",
journal = "Computational Statistics & Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - An algorithm for nonparametric GARCH modelling

AU - Bühlmann, Peter

AU - McNeil, Alexander J.

PY - 2002/10/28

Y1 - 2002/10/28

N2 - A simple iterative algorithm for nonparametric first-order GARCH modelling is proposed. This method offers an alternative to fitting one of the many different parametric GARCH specifications that have been proposed in the literature. A theoretical justification for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical financial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH specification. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested.

AB - A simple iterative algorithm for nonparametric first-order GARCH modelling is proposed. This method offers an alternative to fitting one of the many different parametric GARCH specifications that have been proposed in the literature. A theoretical justification for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical financial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH specification. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested.

KW - GARCH modelling

KW - Nonparametric methods

KW - Volatility estimation

UR - http://www.scopus.com/inward/record.url?scp=0037191009&partnerID=8YFLogxK

U2 - 10.1016/S0167-9473(02)00080-4

DO - 10.1016/S0167-9473(02)00080-4

M3 - Article

AN - SCOPUS:0037191009

VL - 40

SP - 665

EP - 683

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

SN - 0167-9473

IS - 4

ER -