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Forecasting exchange rate volatility using high-frequency data: Is the euro different?

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  • Chortareas, Georgios
  • Jiang, Ying
  • Nankervis, John. C.

Abstract

We assess the performances of alternative procedures for forecasting the daily volatility of the euro's bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high-frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 4 (October)
Pages: 1089-1107

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Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1089-1107

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Euro exchange rates Volatility forecasting High-frequency data GARCH model Long memory time series Forecast evaluation;

References

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Cited by:
  1. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
  2. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, 01.
  3. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements," International Review of Economics & Finance, Elsevier, vol. 30(C), pages 101-119.

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