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Forecasting exchange rates: a robust regression approach

Author

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  • PREMINGER, Arie
  • FRANCK, Raphael

Abstract

The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach,based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictability of twoexchange rates at the 1-, 3- and 6-month horizon. We compare the predictive ability of the robust models to those of the random walk (RW), the standard linear autoregressive (AR) and neural networks (NN) models in terms of forecast accuracy and sign predictability measures. We find that robust models tend to improve the forecasting accuracy of the AR and of theNNat all time horizons, and even of the RWfor forecasts carried out at the 1-month horizon. Robust models are also shown to have significantmarket timing ability at all forecast horizons.
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Suggested Citation

  • PREMINGER, Arie & FRANCK, Raphael, 2007. "Forecasting exchange rates: a robust regression approach," LIDAM Reprints CORE 1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1917
    DOI: 10.1016/j.ijforecast.2006.04.009
    Note: In : International Journal of Forecasting, 23, 71-84, 2007
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    Cited by:

    1. Pavel Čížek, 2013. "Reweighted least trimmed squares: an alternative to one-step estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 514-533, September.
    2. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    3. Cízek, Pavel, 2011. "Semiparametrically weighted robust estimation of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 774-788, January.
    4. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    5. Cizek, P., 2007. "Efficient Robust Estimation of Time-Series Regression Models," Discussion Paper 2007-95, Tilburg University, Center for Economic Research.
    6. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    7. Xian, Lu & He, Kaijian & Lai, Kin Keung, 2016. "Gold price analysis based on ensemble empirical model decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 11-23.
    8. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    9. Robert Gramacy & Samuel W. Malone & Enrique Ter Horst, 2014. "Exchange Rate Fundamentals, Forecasting, And Speculation: Bayesian Models In Black Markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 22-41, January.
    10. Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016. "Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
    11. Antonios K. Alexandridis & Ekaterini Panopoulou & Ioannis Souropanis, 2024. "Forecasting exchange rates: An iterated combination constrained predictor approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 983-1017, July.
    12. Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.
    13. Panagiotis Papaioannnou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Papers 1310.5306, arXiv.org.
    14. Yaojie Zhang & Mengxi He & Yuqi Zhao & Xianfeng Hao, 2023. "Predicting stock realized variance based on an asymmetric robust regression approach," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1022-1047, October.
    15. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    16. Samuel W. Malone & Robert B. Gramacy & Enrique Ter Horst, 2016. "Timing Foreign Exchange Markets," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    17. Mahmoud Dehghan Nayeri & Ali Faal Ghayoumi & Malihe Rostami, 2016. "Forecasting in Financial Data Context," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 4(3), pages 124-133, September.
    18. Saeed Rasekhi, 2011. "Fundamental Modeling Exchange Rate using Genetic Algorithm: A Case Study of European Countries," Journal of Economics and Behavioral Studies, AMH International, vol. 3(6), pages 352-359.
    19. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    20. Tao Wang, 2024. "Nonlinear kernel mode‐based regression for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(2), pages 189-213, March.
    21. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.

    More about this item

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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