Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Vine copula Granger causality in mean

Authors
Jang, H.Kim, J.-M.Noh, H.
Issue Date
Apr-2022
Publisher
Elsevier B.V.
Keywords
Granger causality; Multivariate time series; Semiparametric modeling; Stationary vine copula models
Citation
Economic Modelling, v.109, pp.1 - 10
Journal Title
Economic Modelling
Volume
109
Start Page
1
End Page
10
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/151427
DOI
10.1016/j.econmod.2022.105798
ISSN
0264-9993
Abstract
Ever since the Granger causality test was proposed in 1969, financial market researchers have used it heavily to determine whether the past of a one-time series facilitates the future prediction of another time series. However, as many researchers have noted, the traditional Granger causality test based on the vector autoregression model has limitations in detecting nonlinear causality. To relax the parametric model assumptions of the Granger causality test, nonparametric versions have been proposed to use the advantage of detecting nonlinear Granger causality but have shown difficulty in selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests, we propose the vine copula Granger causality test in mean based on the semiparametric time-series modeling technique. The proposed test overcomes the shortcomings of parametric modeling and has a computational advantage over the nonparametric tests. Our test shows good size and power performance with various simulated data and a real data. © 2022 Elsevier B.V.
Files in This Item
There are no files associated with this item.
Appears in
Collections
이과대학 > 통계학과 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Noh, Hohsuk photo

Noh, Hohsuk
이과대학 (통계학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE