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Conference Paper: On Buffered Time Series Models
| Title | On Buffered Time Series Models |
|---|---|
| Authors | |
| Issue Date | 2017 |
| Citation | HKU-NUS-STANFORD Conference in Statistical Science and Decision Analysics, the University of Hong Kong, Hong Kong, 10-11 March 2017 How to Cite? |
| Abstract | The progress as of today of a new class of threshold time series models known as buffered processes is reviewed. In this new class of models switching back and forth between two regimes depends on two different thresholds. We first investigate the self –excited buffered autoregressive (BAR) process to some extent including an identification procedure and the asymptotic properties of the least squares estimators. We then extend the class of models to cover conditional heteroscedasticty resulting in the buffered GARCH and buffered AR-GARCH models. Simulation studies and applications to real data are considered to illustrate the potential of this new type of threshold models. |
| Description | The HKU-NUS-STANFORD Conference in Statistical Science and Decision Analytics is a research collaboration between the University of Hong Kong, National University of Singapore and Stanford University |
| Persistent Identifier | http://hdl.handle.net/10722/254080 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Li, WK | - |
| dc.date.accessioned | 2018-06-06T01:33:29Z | - |
| dc.date.available | 2018-06-06T01:33:29Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.citation | HKU-NUS-STANFORD Conference in Statistical Science and Decision Analysics, the University of Hong Kong, Hong Kong, 10-11 March 2017 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/254080 | - |
| dc.description | The HKU-NUS-STANFORD Conference in Statistical Science and Decision Analytics is a research collaboration between the University of Hong Kong, National University of Singapore and Stanford University | - |
| dc.description.abstract | The progress as of today of a new class of threshold time series models known as buffered processes is reviewed. In this new class of models switching back and forth between two regimes depends on two different thresholds. We first investigate the self –excited buffered autoregressive (BAR) process to some extent including an identification procedure and the asymptotic properties of the least squares estimators. We then extend the class of models to cover conditional heteroscedasticty resulting in the buffered GARCH and buffered AR-GARCH models. Simulation studies and applications to real data are considered to illustrate the potential of this new type of threshold models. | - |
| dc.language | eng | - |
| dc.relation.ispartof | HKU-NUS-STANFORD Conference in Statistical Science and Decision Analysics | - |
| dc.title | On Buffered Time Series Models | - |
| dc.type | Conference_Paper | - |
| dc.identifier.email | Li, WK: hrntlwk@hkucc.hku.hk | - |
| dc.identifier.authority | Li, WK=rp00741 | - |
| dc.identifier.hkuros | 277923 | - |