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Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?

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Author Info
Georgios Chortareas (University of Essex)
John Nankervis (University of Essex)
Ying Jiang (University of Essex)

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Abstract

This paper focuses on forecasting volatility of high frequency Euro exchange rates. Four 15 minute frequency Euro exchange rate series, including Euro/CHF, Euro/GBP, Euro/JPY and Euro/USD, are used to test the forecast performance of six models, including both traditional time series volatility models and the realized volatility model. Besides the normally used regression test and accuracy test, an equal accuracy test, the HLN-DM test, and a superior predictive ability test are also employed in the out-of-sample forecast evaluation. The FIGARCH model is found to be superior in almost all exchange rate series. Although the widely preferred ARFIMA model shows better performance than the traditional daily volatility models, generally speaking, it cannot surpass the FIGARCH model and the intraday GARCH model. Furthermore, the SVX model does not significantly outperform the SV model in the accuracy test, which contradicts the results of some earlier research. The paper confirms the advantage of using high frequency data and modelling the long memory factor. It also analyses the characteristics of Euro exchange rates and compares the test results with the conclusions drawn by previous studies

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Paper provided by Money Macro and Finance Research Group in its series Money Macro and Finance (MMF) Research Group Conference 2006 with number 79.

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Date of creation: 02 Feb 2007
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Handle: RePEc:mmf:mmfc06:79

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Keywords: exchange rates; volatility; euro; high frequency;

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Find related papers by JEL classification:
F31 - International Economics - - International Finance - - - Foreign Exchange
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Jose A. Lopez, 1995. "Evaluating the predictive accuracy of volatility models," Research Paper 9524, Federal Reserve Bank of New York. [Downloadable!]
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  2. Lobato, Ignacio & Nankervis, John C & Savin, N E, 2001. "Testing for Autocorrelation Using a Modified Box-Pierce Q Test," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 187-205, February.
  3. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute. [Downloadable!]
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  4. Beltratti, Andrea & Morana, Claudio, 1999. "Computing value at risk with high frequency data," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 431-455, December. [Downloadable!] (restricted)
  5. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June. [Downloadable!] (restricted)
  6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June. [Downloadable!] (restricted)
  7. Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute. [Downloadable!]
  8. Kenneth D. West & Dongchul Cho, 1994. "The Predictive Ability of Several Models of Exchange Rate Volatility," NBER Technical Working Papers 0152, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  9. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
    Other versions:
  10. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September. [Downloadable!] (restricted)
  11. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December. [Downloadable!] (restricted)
  12. Yu, Jun, 2002. "Forecasting Volatility in the New Zealand Stock Market," Applied Financial Economics, Taylor and Francis Journals, vol. 12(3), pages 193-202, March. [Downloadable!] (restricted)
  13. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  14. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Blackwell Publishing, vol. 18(5), pages 613-649, December. [Downloadable!] (restricted)
    Other versions:
  15. Taisei Kaizoji & Thomas Lux, 2004. "Forecasting Volume and Volatility in the Tokyo Stock Market: The Advantage of Long Memory Models," Computing in Economics and Finance 2004 158, Society for Computational Economics.
  16. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  17. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July. [Downloadable!] (restricted)
  18. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," NBER Working Papers 7488, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  19. Eugenie Hol & Siem Jan Koopman, 2000. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility," Tinbergen Institute Discussion Papers 00-104/4, Tinbergen Institute. [Downloadable!]
  20. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-061, New York University, Leonard N. Stern School of Business-. [Downloadable!]
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