Abstract
This article introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroscedasticity (BAR-GARCH). The proposed model, as an extension of the BAR model in Li et al. (2015), can capture the buffering phenomena of time series in both the conditional mean and variance. Thus, it provides us a new way to study the nonlinearity of time series. Compared with the existing AR-GARCH and threshold AR-GARCH models, an application to several exchange rates highlights the importance of the BAR-GARCH model. Copyright © 2017 American Statistical Association.
Original language | English |
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Pages (from-to) | 528-542 |
Journal | Journal of Business and Economic Statistics |
Volume | 35 |
Issue number | 4 |
Early online date | Apr 2017 |
DOIs | |
Publication status | Published - 2017 |
Citation
Zhu, K., Li, W. K., & Yu, P. L. H. (2017). Buffered autoregressive models with conditional heteroscedasticity: An application to exchange rates. Journal of Business & Economic Statistics, 35(4), 528-542. doi: 10.1080/07350015.2015.1123634Keywords
- Buffered AR-GARCH model
- Buffered AR model
- Exchange rate
- GARCH model
- Nonlinear time series
- Threshold AR model