Journal article
On conditionally heteroscedastic AR models with thresholds
Statistica Sinica, Vol.24(2), pp.625-652
2014
DOI: 10.5705/ss.2012.185
Abstract
Conditional heteroscedasticity is prevalent in many time series. By view- ing conditional heteroscedasticity as the consequence of a dynamic mixture of in- dependent random variables, we develop a simple yet versatile observable mixing function, leading to the conditionally heteroscedastic AR model with thresholds, or a T-CHARM for short. We demonstrate its many attributes and provide com- prehensive theoretical underpinnings with efficient computational procedures and algorithms. We compare, via simulation, the performance of T-CHARM with the GARCH model. We report some experiences using data from economics, biology, and geoscience.
Details
- Title: Subtitle
- On conditionally heteroscedastic AR models with thresholds
- Creators
- Kung Sik ChanDong LiShiqing LingHowell Tong
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.24(2), pp.625-652
- DOI
- 10.5705/ss.2012.185
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Language
- English
- Date published
- 2014
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9983985817502771
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