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

International Journal of Forecasting

Volumn 27, Issue 4, 2011, Pages 1089-1107

  Chortareas, Georgios ^a   Jiang, Ying ^b   Nankervis, John C ^c  

^a UNIVERSITY OF ATHENS   (Greece)

^b University of Nottingham   (China)

^c UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

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

Indexed keywords

EID: 80052175874     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2010.07.003     Document Type: Article

References (55)

1. Andersen T.G., Bollerslev T. Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance 1997, 4:115-158.
2. Andersen T.G., Bollerslev T. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review 1998, 39:885-905.
3. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (1999). (Understanding, optimizing, using and forecasting) realized volatility and correlation. Manuscript, Northwestern University, Duke University and University of Pennsylvania. Published in revised form as "Great realizations". Risk. March 2000 (pp. 105-108).
4. Andersen T.G., Bollerslev T., Diebold F.X., Labys P. Exchange rate returns standardized by realized volatility are (nearly) Gaussian. Multinational Finance Journal 2000, 4:159-179.
5. Andersen T.G., Bollerslev T., Diebold F.X., Labys P. The distribution of exchange rate volatility. Journal of the American Statistical Association 2001, 96:42-55.
6. Andersen T.G., Bollerslev T., Diebold F.X., Labys P. Modelling and forecasting realized volatility. Econometrica 2003, 71:579-625.
7. Baillie R.T., Cencen A.A., Han Y.W. High frequency Deutsche Mark-US Dollar returns: FIGARCH representations and non-linearities. Multinational Finance Journal 2000, 4:247-267.
8. Balaban E. Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate. Economic Letters 2004, 83(1):99-105.
9. Bandi F.M., Russell J.R. Separating microstructure noise from volatility. Journal of Financial Economics 2005, 79:655-692.
10. Barbosa, P. A. (2002). Investigating high frequency exchange rate from the Brazilian Sisbex market. Working paper. http://ssrn.com/abstract%3D428581.
11. Bauwens L., Sucarrat G. General to specific modelling of exchange rate volatility: a forecast evaluation. International Journal of Forecasting 2010, 26(4):885-907.
12. Beltratti A., Morana C. Computing value at risk with high frequency data. Journal of Empirical Finance 1999, 6:431-455.
13. Broto C., Ruiz E. Estimation methods for stochastic volatility models: a survey. Journal of Economic Surveys 2004, 18(4):613-649.
14. Clements M.P., Galvão A.B., Kim J.H. Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. Journal of Empirical Finance 2008, 15(4):729-750.
15. Corsi F. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 2009, 7(2):174-196.
16. Dacorogna M.M., Gencay R., Muller U.A., Olsen R.B., Pictec O.V. An introduction to high-frequency finance 2001, Academic Press, San Diego.
17. Diebold F.X., Mariano R.S. Comparing predictive accuracy. Journal of Business and Economic Statistics 1995, 13:253-265.
18. Doornik J.A. Object-oriented matrix programming using Ox 3.0 2001, Timberlake Consultants Press, London.
19. Doornik, J. A., & Ooms, M. (2001). A package for estimating, forecasting and simulating ARFIMA models: ARFIMA package 1.01 for Ox. Package Manual.
20. Dunis C.L., Laws J., Chauvin S. The use of market data and model combination to improve forecast accuracy. Developments in forecast combination and portfolio choice 2001, Wiley, Chichester. C.L. Dunis, A. Timmermann, J.E. Moody (Eds.).
21. Francq C., Roy R., Zakoian J.M. Diagnostic checking in ARMA models with uncorrelated errors. Journal of the American Statistical Association 2005, 100:532-544.
22. Ghysels E., Sinko A., Valkanov R. MIDAS regressions: further results and new directions. Econometric Reviews 2007, 26(1):53-90.
23. Granger C.W.J., Joyeux R. An introduction to long memory time series models and fractional differencing. Journal of Time Series Analysis 1980, 1:15-39.
24. Hansen P.R. A test for superior predictive ability. Journal of Business and Economic Statistics 2005, 23:365-380.
25. Hansen, P. R., Kim, J., & Lunde, A. (2003). Testing for superior predictive ability using Ox, a manual for SPA for Ox. Package Manual.
26. Harvey D.I., Leybourne S.J., Newbold P. Testing the equality of prediction mean squared errors. International Journal of Forecasting 1997, 13:281-291.
27. Hatanaka M. An efficient estimator for the dynamic adjustment model with autocorrelated errors. Journal of Econometrics 1974, 2:199-220.
28. Heaney R., Pattenden K. Change in unconditional foreign exchange rate volatility: an analysis of the GBP and USD price of the euro from 2002 to 2003. Applied Economics Letters 2005, 12:929-932.
29. Hol, E., & Koopman, S. J. (2000). Forecasting the variability of stock index returns with stochastic volatility models and implied volatility. Tinbergen Institute Discussion Papers No. 00-104/4.
30. Hol, E., & Koopman, S. J. (2002). Stock index volatility forecasting with high frequency data. Tinbergen Institute Discussion Paper No. 2002-068/4.
31. Jones, B. (2003). Is ARCH useful in high frequency foreign exchange applications? Research paper No. 24. Applied Finance Centre, Macquarie University.
32. Kayahan B., Stengos T. Intra-day features of realized volatility: evidence from an emerging market. International Journal of Business and Economics 2002, 1(1):17-24.
33. Koopman S.J., Jungbacker B., Hol E. Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance 2005, 12(3):445-475.
34. Koopman S.J., Shephard N., Doornik J.A. Statistical algorithms for models in state space using SsfPack 2.2. Econometrics Journal 1999, 2:113-166.
35. Laurent, S., & Peters, J. P. (2005). G@RCH 4.0, estimating and forecasting ARCH models. Timberlake Consultants. http://www.timberlake.co.uk.
36. Ljung G., Box G. On a measure of lack of fit in time series models. Biometrika 1979, 66:265-270.
37. Lobato I., Nankervis J.C., Savin N.E. Testing for autocorrelation using a modified Box-Pierce Q test. International Economic Review 2001, 42:187-205.
38. Lopez J.A. Evaluating the predictive accuracy of volatility models. Journal of Forecasting 2001, 20(2):87-109.
39. Lux, T., & Kaizoji, T. (2004). Forecasting volume and volatility in the Tokyo stock market: the advantage of long memory models. In Computing in Economics and Finance 2004. Society for Computational Economics, No. 158.
40. Marlik A.K. European exchange rate volatility dynamics: an empirical investigation. Journal of Empirical Finance 2005, 12:187-215.
41. Martens M. Forecasting daily exchange rate volatility using intraday returns. Journal of International Money and Finance 2001, 20(1):1-23.
42. Martens M., Chang Y.C., Taylor S. A comparison of seasonal adjustment methods when forecasting intraday volatility. Journal of Financial Research 2002, 25(2):283-299.
43. Martens M., Zein J. Predicting financial volatility: high-frequency time-series forecasts vis-à-vis implied volatility. Journal of Futures Markets 2004, 24:1005-1028.
44. McLeod A.I. On the distribution of residual autocorrelations in Box-Jenkins method. Journal of the Royal Statistical Society, Series B 1978, 40:296-302.
45. Mincer, J., & Zarnowitz, V. (1969). The evaluation of economic forecasts. In Economic forecasts and expectations. New York: National Bureau of Economic Research.
46. Pong S., Shackleton M., Taylor S.J., Xu X. Forecasting currency volatility: a comparison of implied volatilities and AR(FI)MA models. Journal of Banking and Finance 2004, 28(9):2541-2563.
47. Poon S.H., Granger C. Forecasting financial market volatility: a review. Journal of Economic Literature 2003, 41(2):478-539.
48. Rahman S., Ang K.P. Intraday return volatility process: evidence from NASDAQ stocks. Review of Quantitative Finance and Accounting 2002, 19:155-180.
49. Taylor S. Modelling stochastic volatility: a review and comparative study. Mathematical Finance 1994, 4:183-204.
50. Taylor S., Xu X. The incremental volatility information in one million foreign exchange quotations. Journal of Empirical Finance 1997, 4:317-340.
51. Vilasuso J. Forecasting exchange rate volatility. Economics Letters 2002, 76:59-64.
52. West K.D., Cho D. The predictive ability of several models of exchange rate volatility. Journal of Econometrics 1995, 69:367-391.
53. Yu J. Forecasting volatility in the New Zealand stock market. Applied Financial Economics 2002, 12:193-202.
54. Zhang L., Mykland P.A., Aït-Sahalia Y. A tale of two time scales: determining integrated volatility with noisy high frequency data. Journal of the American Statistical Association 2005, 100:1394-1411.
55. Zumbach G. Volatility processes and volatility forecast with long memory. Quantitative Finance 2004, 4(1):70-86.