Asymptotic variance-covariance matrix of sample autocorrelations for threshold-asymmetric GARCH processes
- Authors
- Hwang, S. Y.; Baek, J. S.
- Issue Date
- Feb-2009
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- joint asymptotic distribution; sample autocorrelations; squared process; threshold-asymmetric GARCH
- Citation
- STATISTICS, v.43, no.1, pp 35 - 51
- Pages
- 17
- Journal Title
- STATISTICS
- Volume
- 43
- Number
- 1
- Start Page
- 35
- End Page
- 51
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14110
- DOI
- 10.1080/02331880802172818
- ISSN
- 0233-1888
1029-4910
- Abstract
- In the field of financial time series, threshold-asymmetric conditional variance models can be used to explain asymmetric volatilities [C.W. Li and W.K. Li, On a double-threshold autoregressive heteroscedastic time series model, J. Appl. Econometrics 11 (1996), pp. 253-274]. In this paper, we consider a broad class of threshold-asymmetric GARCH processes (TAGARCH, hereafter) including standard ARCH and GARCH models as special cases. Since sample autocorrelation function provides a useful information to identify an appropriate time-series model for the data, we derive asymptotic distributions of sample autocorrelations both for original process and for squared process. It is verified that standard errors of sample autocorrelations for TAGARCH models are significantly different from unity for lower lags and they are exponentially converging to unity for higher lags. Furthermore they are shown to be asymptotically dependent while being independent of standard GARCH models. These results will be interesting in the light of the fact that TAGARCH processes are serially uncorrelated. A simulation study is reported to illustrate our results.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 이과대학 > 통계학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.