Sequential change-point detection in time series models with conditional heteroscedasticity

Youngmi Lee; Sungdon Kim; Haejune Oh

doi:10.1016/j.econlet.2024.111597

Sequential change-point detection in time series models with conditional heteroscedasticity

Youngmi Lee
, Sungdon Kim
, Haejune Oh^*

^*Corresponding author for this work

Department of Statistics

Gyeongsang National University

Research output: Contribution to journal › Journal article › peer-review

1 Scopus citations

Abstract

In this study, we investigate a sequential procedure for the early detection of parameter changes in conditionally heteroscedastic time series models. We introduce the detectors based on the cumulative sum of score vectors and residuals for this procedure. The asymptotic properties of the monitoring procedures are established under the null and alternative hypotheses. Simulation results are provided for illustration.

Original language	English
Article number	111597
Journal	Economics Letters
Volume	236
DOIs	https://doi.org/10.1016/j.econlet.2024.111597
State	Published - 2024.03

Keywords

Asymmetric GARCH
Conditionally heteroscedastic time series
GARCH-type models
Parameter change
Sequential detection

Quacquarelli Symonds(QS) Subject Topics

Accounting & Finance
Economics & Econometrics

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10.1016/j.econlet.2024.111597

Cite this

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title = "Sequential change-point detection in time series models with conditional heteroscedasticity",

abstract = "In this study, we investigate a sequential procedure for the early detection of parameter changes in conditionally heteroscedastic time series models. We introduce the detectors based on the cumulative sum of score vectors and residuals for this procedure. The asymptotic properties of the monitoring procedures are established under the null and alternative hypotheses. Simulation results are provided for illustration.",

keywords = "Asymmetric GARCH, Conditionally heteroscedastic time series, GARCH-type models, Parameter change, Sequential detection",

author = "Youngmi Lee and Sungdon Kim and Haejune Oh",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier B.V.",

year = "2024",

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doi = "10.1016/j.econlet.2024.111597",

language = "English",

volume = "236",

journal = "Economics Letters",

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T1 - Sequential change-point detection in time series models with conditional heteroscedasticity

AU - Lee, Youngmi

AU - Kim, Sungdon

AU - Oh, Haejune

PY - 2024/3

Y1 - 2024/3

N2 - In this study, we investigate a sequential procedure for the early detection of parameter changes in conditionally heteroscedastic time series models. We introduce the detectors based on the cumulative sum of score vectors and residuals for this procedure. The asymptotic properties of the monitoring procedures are established under the null and alternative hypotheses. Simulation results are provided for illustration.

AB - In this study, we investigate a sequential procedure for the early detection of parameter changes in conditionally heteroscedastic time series models. We introduce the detectors based on the cumulative sum of score vectors and residuals for this procedure. The asymptotic properties of the monitoring procedures are established under the null and alternative hypotheses. Simulation results are provided for illustration.

KW - Asymmetric GARCH

KW - Conditionally heteroscedastic time series

KW - GARCH-type models

KW - Parameter change

KW - Sequential detection

UR - https://www.scopus.com/pages/publications/85184614784

U2 - 10.1016/j.econlet.2024.111597

DO - 10.1016/j.econlet.2024.111597

M3 - Journal article

AN - SCOPUS:85184614784

SN - 0165-1765

VL - 236

JO - Economics Letters

JF - Economics Letters

M1 - 111597

ER -

Sequential change-point detection in time series models with conditional heteroscedasticity

Abstract

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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