Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model

Annastiina Silvennoinen; Timo Teräsvirta

doi:10.1016/j.ecosta.2021.07.008

Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model

Annastiina Silvennoinen^*, Timo Teräsvirta

^*Corresponding author af dette arbejde

Institut for Økonomi - CREATES

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

6 Citationer (Scopus)

Abstract

A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.

Originalsprog	Engelsk
Tidsskrift	Econometrics and Statistics
Vol/bind	32
Sider (fra-til)	57-72
Antal sider	16
ISSN	2468-0389
DOI	https://doi.org/10.1016/j.ecosta.2021.07.008
Status	Udgivet - okt. 2024

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10.1016/j.ecosta.2021.07.008

https://authors.elsevier.com/c/1ddE68jbm9BkYG

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title = "Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model",

abstract = "A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.",

keywords = "Deterministically varying correlation, Multiplicative time-varying GARCH, Multivariate GARCH, Nonstationary volatility, Smooth transition GARCH",

author = "Annastiina Silvennoinen and Timo Ter{\"a}svirta",

year = "2024",

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doi = "10.1016/j.ecosta.2021.07.008",

language = "English",

volume = "32",

pages = "57--72",

journal = "Econometrics and Statistics",

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Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model. / Silvennoinen, Annastiina; Teräsvirta, Timo.
I: Econometrics and Statistics, Bind 32, 10.2024, s. 57-72.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model

AU - Silvennoinen, Annastiina

AU - Teräsvirta, Timo

PY - 2024/10

Y1 - 2024/10

N2 - A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.

AB - A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.

KW - Deterministically varying correlation

KW - Multiplicative time-varying GARCH

KW - Multivariate GARCH

KW - Nonstationary volatility

KW - Smooth transition GARCH

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U2 - 10.1016/j.ecosta.2021.07.008

DO - 10.1016/j.ecosta.2021.07.008

M3 - Journal article

AN - SCOPUS:85122658927

SN - 2468-0389

VL - 32

SP - 57

EP - 72

JO - Econometrics and Statistics

JF - Econometrics and Statistics

ER -

Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model

Abstract

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