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A Double-Threshold GARCH Model for the French Franc/Deutschmark Exchange Rate

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  • Brooks, Chris

Abstract

This paper combines and generalizes a number of recent time series models of daily exchange rate series by using a SETAR model which also allows the variance equation of a GARCH specification for the error terms to be drawn from more than one regime. An application of the model to the French Franc/Deutschmark exchange rate demonstrates that out-of-sample forecasts for the exchange rate volatility are also improved when the restriction that the data it is drawn from a single regime is removed. This result highlights the importance of considering both types of regime shift (i.e. thresholds in variance as well as in mean) when analysing financial time series. Copyright © 2001 by John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 20 (2001)
Issue (Month): 2 (March)
Pages: 135-43

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Handle: RePEc:jof:jforec:v:20:y:2001:i:2:p:135-43

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Antonio Rubia Serrano & Trino-Manuel Ñíguez, 2003. "Forecasting The Conditional Covariance Matrix Of A Portfolio Under Long-Run Temporal Dependence," Working Papers. Serie AD 2003-34, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  2. Cathy Chen & Richard Gerlach, 2013. "Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity," Computational Statistics, Springer, vol. 28(3), pages 1103-1131, June.
  3. Zhu, Ke & Li, Wai Keung & Yu, Philip L.H., 2014. "Buffered autoregressive models with conditional heteroscedasticity: An application to exchange rates," MPRA Paper 53874, University Library of Munich, Germany.
  4. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2008. "Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns," SFB 649 Discussion Papers SFB649DP2008-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Modelling squared returns using a SETAR model with long-memory dynamics," Economics Letters, Elsevier, vol. 86(2), pages 237-243, February.
  6. Sitzia, Bruno & Iovino, Doriana, 2008. "Nonlinearities in Exchange rates: Double EGARCH Threshold Models for Forecasting Volatility," MPRA Paper 8661, University Library of Munich, Germany.
  7. W. K. Li & Shiqing Ling & Michael McAleer, 2001. "A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors," ISER Discussion Paper 0545, Institute of Social and Economic Research, Osaka University.
  8. Gerlach, Richard & Chen, Cathy W.S. & Lin, Doris S.Y. & Huang, Ming-Hsiang, 2006. "Asymmetric responses of international stock markets to trading volume," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 422-444.
  9. Suardi, Sandy, 2008. "Central bank intervention, threshold effects and asymmetric volatility: Evidence from the Japanese yen-US dollar foreign exchange market," Economic Modelling, Elsevier, vol. 25(4), pages 628-642, July.
  10. Amélie Charles, 2010. "The day-of-the week effects on the volatility: The role of the asymmetry," Post-Print hal-00771136, HAL.
  11. Zhu, Junjun & Xie, Shiyu, 2010. "Bayesian Analysis of a Triple-Threshold GARCH Model with Application in Chinese Stock Market," MPRA Paper 28235, University Library of Munich, Germany.
  12. Toshio Utsunomiya, 2013. "A new approach to the effect of intervention frequency on the foreign exchange market: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 45(26), pages 3742-3759, September.
  13. Chen, Qian & Gerlach, Richard H., 2013. "The two-sided Weibull distribution and forecasting financial tail risk," International Journal of Forecasting, Elsevier, vol. 29(4), pages 527-540.

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