Buffered Autoregressive Models With Conditional Heteroscedasticity: An Application to Exchange Rates |
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| Authors: | Ke Zhu Wai Keung Li Philip L H Yu |
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| Institution: | 1. Institute of Applied Mathematics, Chinese Academy of Sciences, Haidian District, Zhongguancun, Beijing, China (kzhu@amss.ac.cn);2. Department of Statistics and Actuarial Science, University of Hong Kong, Pokfulam Road, Hong Kong (hrntlwk@hku.hk;3. plhyu@hku.hk) |
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| 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 Li, G.D., Guan, B., Li, W.K., and Yu, P. L.H. (2015), “Hysteretic Autoregressive Time Series Models,” Biometrika, 102, 717–723.Crossref], PubMed], Web of Science ®] , Google Scholar]), 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. |
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| Keywords: | Buffered AR-GARCH model Buffered AR model Exchange rate GARCH model Nonlinear time series Threshold AR model |
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