Buffered autoregressive models with conditional heteroscedasticity: An application to exchange rates

Ke ZHU, Wai Keung LI, Philip L. H. YU

Research output: Contribution to journalArticle

7 Citations (Scopus)

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 languageEnglish
Pages (from-to)528-542
JournalJournal of Business and Economic Statistics
Volume35
Issue number4
Early online dateApr 2017
DOIs
Publication statusPublished - 2017

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Conditional Heteroscedasticity
Exchange rate
Autoregressive Model
Generalized Autoregressive Conditional Heteroscedasticity
Time series
AR Model
GARCH Model
time series
Model
Nonlinearity
Conditional heteroscedasticity
Autoregressive model
Exchange rates
Generalized autoregressive conditional heteroscedasticity

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: 10.1080/07350015.2015.1123634

Keywords

  • Buffered AR-GARCH model
  • Buffered AR model
  • Exchange rate
  • GARCH model
  • Nonlinear time series
  • Threshold AR model