1. bookVolumen 22 (2022): Edición 4 (November 2022)
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1314-4081
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13 Mar 2012
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Modelling and Forecasting of EUR/USD Exchange Rate Using Ensemble Learning Approach

Publicado en línea: 10 Nov 2022
Volumen & Edición: Volumen 22 (2022) - Edición 4 (November 2022)
Páginas: 142 - 151
Recibido: 27 Jun 2022
Aceptado: 02 Oct 2022
Detalles de la revista
License
Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés

1. Chortareas, G., Y. Jiang, J. C. Nankervis. Forecasting Exchange Rate Volatility Using High-Frequency Data: Is the Euro Different? – International Journal of Forecasting, Vol. 27, 2011, No 4, pp. 1089-1107.10.1016/j.ijforecast.2010.07.003 Search in Google Scholar

2. Moosa, I. A., J. J. Vaz. Cointegration, Error Correction and Exchange Rate Forecasting. – Journal of International Financial Markets, Institutions and Money, Vol. 44, 2016, pp. 21-34.10.1016/j.intfin.2016.04.007 Search in Google Scholar

3. Byrne, J. P., D. Korobilis, P. J. Ribeir. Exchange Rate Predictability in a Changing World. – Journal of International Money and Finance, Vol. 62, 2016, pp. 1-24.10.1016/j.jimonfin.2015.12.001 Search in Google Scholar

4. Jian, Z., P. Deng, K. Luo, Z. Zhu. The Effect of Market Quality on the Causality between Returns and Volatilities: Evidence from CSI 300 Index Futures. – Journal of Management Science and Engineering, Vol. 3, 2018, No 1, pp. 16-38. Search in Google Scholar

5. Galeshchuk, S. Neural Networks Performance in Exchange Rate Prediction. – Neurocomputing, Vol. 172, 2016, pp. 446-452.10.1016/j.neucom.2015.03.100 Search in Google Scholar

6. Shen, F., J. Chao, J. Zhao. Forecasting Exchange Rate Using Deep Belief Networks and Conjugate Gradient Method. – Neurocomputing, Vol. 167, 2015, pp. 243-253.10.1016/j.neucom.2015.04.071 Search in Google Scholar

7. Dunis, C. L., J. Laws, G. Sermpinis. Higher Order and Recurrent Neural Architectures for Trading the EUR/USD Exchange Rate. – Quantitative Finance, Vol. 11, 2011, No 4, pp. 615-629.10.1080/14697680903386348 Search in Google Scholar

8. Sermpinis, G., C. Stasinakis, K. Theofilatos, A. Karathanasopoulos. Modeling, Forecasting and Trading the EUR Exchange Rates with Hybrid Rolling Genetic Algorithms – Support Vector Regression Forecast Combinations. – European Journal of Operational Research, Vol. 247, 2015, No 3, pp. 831-846.10.1016/j.ejor.2015.06.052 Search in Google Scholar

9. Liu, F., A. A. Pantelous, H. J. von Mettenheim. Forecasting and Trading High Frequency Volatility on Large Indices. – Quantitative Finance, Vol. 18, 2018, No 5, pp. 737-748.10.1080/14697688.2017.1414489 Search in Google Scholar

10. Zhou, Z.-H. Ensemble Methods: Foundations and Algorithms. Boca Raton, CRC Press, 2012.10.1201/b12207 Search in Google Scholar

11. Opitz, D., R. Maclin. Popular Ensemble Methods: An Empirical Study. – Journal of Artificial Intelligence Research, Vol. 11, 1999, pp. 169-198.10.1613/jair.614 Search in Google Scholar

12. Yu, L., K. K. Lai, S. Y. Wang. Multistage RBF Neural Network Ensemble Learning for Exchange Rates Forecasting. – Neurocomputing, Vol. 71, 2008, No 16-18, pp. 3295-3302.10.1016/j.neucom.2008.04.029 Search in Google Scholar

13. Sun, S. L., S. Y. Wang, Y. J. Wei, G. W. Zhang. A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting. – IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 50, 2018, No 6, pp. 2284-2292.10.1109/TSMC.2018.2799869 Search in Google Scholar

14. Sun, S., S. Wang, Y. Wei. A New Ensemble Deep Learning Approach for Exchange Rates Forecasting and Trading. – Advanced Engineering Informatics, Vol. 46, 2020, Art. No 101160.10.1016/j.aei.2020.101160 Search in Google Scholar

15. Box, G. E. P., G. M. Jenkins, G. S. Reinsel. Time Series Analysis, Forecasting and Control. 3th Ed. New Jersey, Prentice-Hall, 1994. Search in Google Scholar

16. Breiman, L. Bagging Predictors. – Machine Learning, Vol. 24, 1996, No 2, pp. 123-140.10.1007/BF00058655 Search in Google Scholar

17. Breiman, L., J. Friedman, R. Olshen, C. Stone. Classification and Regression Trees. Boca Raton, Wadsworth Books-CRC, 1984. Search in Google Scholar

18. Wolfram Mathematica (Online, Accessed on 22 June 2022). https://www.wolfram.com/mathematica/ Search in Google Scholar

19. Brockwell, P. J., R. A. Davis. Introduction to Time Series and Forecasting. 3th Ed. New York, Springer, 2016.10.1007/978-3-319-29854-2 Search in Google Scholar

20. Gocheva-Ilieva, S. G., D. S. Voynikova, M. P. Stoimenova, A. V. Ivanov, I. P. Iliev. Regression Trees Modeling of Time Series for Air Pollution Analysis and Forecasting. – Neural Computing and Applications, Vol. 31, 2019, No 12, pp. 9023-9039.10.1007/s00521-019-04432-1 Search in Google Scholar

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