Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study

Torben G. Andersen; Hyung Jin Chung; Bent E. Sørensen

doi:10.1016/S0304-4076(98)00049-9

Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study

Torben G. Andersen, Hyung Jin Chung, Bent E. Sørensen^*

^*Corresponding author for this work

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Abstract

We perform an extensive Monte Carlo study of efficient method of moments (EMM) estimation of a stochastic volatility model. EMM uses the expectation under the structural model of the score from an auxiliary model as moment conditions. We examine the sensitivity to the choice of auxiliary model using ARCH, GARCH, and EGARCH models for the score as well as nonparametric extensions. EMM efficiency approaches that of maximum likelihood for larger sample sizes. Inference is sensitive to the choice of auxiliary model in small samples, but robust in larger samples. Specification tests and 't-tests' show little size distortion.

Original language	English (US)
Pages (from-to)	61-87
Number of pages	27
Journal	Journal of Econometrics
Volume	91
Issue number	1
DOIs	https://doi.org/10.1016/S0304-4076(98)00049-9
State	Published - Jul 1999

Keywords

EMM
GMM
Monte Carlo
Stochastic volatility

ASJC Scopus subject areas

Economics and Econometrics

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10.1016/S0304-4076(98)00049-9

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@article{5c388f6c39784ac8b6793f92ee8d1d81,

title = "Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study",

abstract = "We perform an extensive Monte Carlo study of efficient method of moments (EMM) estimation of a stochastic volatility model. EMM uses the expectation under the structural model of the score from an auxiliary model as moment conditions. We examine the sensitivity to the choice of auxiliary model using ARCH, GARCH, and EGARCH models for the score as well as nonparametric extensions. EMM efficiency approaches that of maximum likelihood for larger sample sizes. Inference is sensitive to the choice of auxiliary model in small samples, but robust in larger samples. Specification tests and 't-tests' show little size distortion.",

keywords = "EMM, GMM, Monte Carlo, Stochastic volatility",

author = "Andersen, {Torben G.} and Chung, {Hyung Jin} and S{\o}rensen, {Bent E.}",

note = "Funding Information: We are grateful to Hyung-Kwon Chung for assistance and David Tom for programming the EGARCH model in C++. The second author gratefully acknowledges financial assistance from the Ehrlich Foundation. We thank the editor, Ron Gallant, an associate editor, three anonymous referees, as well as Moshe Buchinsky, Neil Shephard, Doug Steigerwald, and seminar participants at Brown University, Yale University, University of Rochester, the NBER 1997 Summer Institute and the Econometric Society Winter Meetings, Chicago, January 1998, for comments.",

year = "1999",

month = jul,

doi = "10.1016/S0304-4076(98)00049-9",

language = "English (US)",

volume = "91",

pages = "61--87",

journal = "Journal of Econometrics",

issn = "0304-4076",

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TY - JOUR

T1 - Efficient method of moments estimation of a stochastic volatility model

T2 - A Monte Carlo study

AU - Andersen, Torben G.

AU - Chung, Hyung Jin

AU - Sørensen, Bent E.

N1 - Funding Information: We are grateful to Hyung-Kwon Chung for assistance and David Tom for programming the EGARCH model in C++. The second author gratefully acknowledges financial assistance from the Ehrlich Foundation. We thank the editor, Ron Gallant, an associate editor, three anonymous referees, as well as Moshe Buchinsky, Neil Shephard, Doug Steigerwald, and seminar participants at Brown University, Yale University, University of Rochester, the NBER 1997 Summer Institute and the Econometric Society Winter Meetings, Chicago, January 1998, for comments.

PY - 1999/7

Y1 - 1999/7

N2 - We perform an extensive Monte Carlo study of efficient method of moments (EMM) estimation of a stochastic volatility model. EMM uses the expectation under the structural model of the score from an auxiliary model as moment conditions. We examine the sensitivity to the choice of auxiliary model using ARCH, GARCH, and EGARCH models for the score as well as nonparametric extensions. EMM efficiency approaches that of maximum likelihood for larger sample sizes. Inference is sensitive to the choice of auxiliary model in small samples, but robust in larger samples. Specification tests and 't-tests' show little size distortion.

AB - We perform an extensive Monte Carlo study of efficient method of moments (EMM) estimation of a stochastic volatility model. EMM uses the expectation under the structural model of the score from an auxiliary model as moment conditions. We examine the sensitivity to the choice of auxiliary model using ARCH, GARCH, and EGARCH models for the score as well as nonparametric extensions. EMM efficiency approaches that of maximum likelihood for larger sample sizes. Inference is sensitive to the choice of auxiliary model in small samples, but robust in larger samples. Specification tests and 't-tests' show little size distortion.

KW - EMM

KW - GMM

KW - Monte Carlo

KW - Stochastic volatility

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U2 - 10.1016/S0304-4076(98)00049-9

DO - 10.1016/S0304-4076(98)00049-9

M3 - Article

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SN - 0304-4076

VL - 91

SP - 61

EP - 87

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -

Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study

Abstract

Keywords

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