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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.

Suggested Citation

  • Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1089-1107
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    Cited by:

    1. 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.
    2. 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.
    3. 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, January.
    4. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    5. repec:eee:phsmap:v:489:y:2018:i:c:p:78-93 is not listed on IDEAS
    6. Ying Jiang & Shamim Ahmed & Xiaoquan Liu, 2017. "Volatility forecasting in the Chinese commodity futures market with intraday data," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 1123-1173, May.
    7. Chkili, Walid & Aloui, Chaker & Nguyen, Duc Khuong, 2012. "Asymmetric effects and long memory in dynamic volatility relationships between stock returns and exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 738-757.

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