Open Access
SHS Web Conf.
Volume 61, 2019
Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018)
Article Number 01012
Number of page(s) 10
Section Strategic Partnerships in International Trade
Published online 30 January 2019
  1. M. Sheikhan, N. Mohammadi, Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data: revue littéraire mensuelle. Neural Computing and Applications, 23(3-4), 1185-1194, (2013) [CrossRef] [Google Scholar]
  2. S. De Baets, N. Harvey, Forecasting from time series subject to sporadic perturbations:Effectiveness of different types of forecasting support. International Journal of Forecasting, 34(2), 163-180, (2018) [CrossRef] [Google Scholar]
  3. A. León-Álvarez, J. Betancur-Gómez, F. Jaimes-Barragán, H. Grisales-Romero, Ronda clínica y epidemiológica. Series de tiempo. IATREIA, 29(3), (2016) [Google Scholar]
  4. M. Vochozka, Formation of complex company evaluation method through neural networks based on the example of construction companies’ collection. AD ALTA -Journal of Interdisciplinary Research, 7(2), 232-239, (2017) [Google Scholar]
  5. F. Rodrigues, I. Markou, F. C. Pereira, Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach. Information Fusion, 49, 120-129, (2019) [CrossRef] [Google Scholar]
  6. P. Rostan, A. Rostan, The versatility of spectrum analysis for forecasting financial time series. Journal of Forecasting, 37(3), 327-339, (2108) [Google Scholar]
  7. K. Jebran, A. Iqbal, Dynamics of volatility spillover between stock market and foreign exchange market: evidence from Asian Countries. Financial Innovation, 2(1), (2016) [CrossRef] [Google Scholar]
  8. X. Chen, The Globalization of the Chinese Yuan (CNY) and Its Rising Role in the International Currency System. China and WTO Review, 2(2), 303-320, (2016) [CrossRef] [Google Scholar]
  9. M. Mandel, V. Quang Tran, Empirická verifikace exportní funkce s akcentem na vliv kurzu české koruny k euru. Politická ekonomie, 65(6), 649-668, (2017) [Google Scholar]
  10. J. Cimburek, P. Řežábek, Hotovost v oběhu: světové trendy a situace v České republice. Politická ekonomie, 56(6), 739-758, (2008) [CrossRef] [Google Scholar]
  11. Czech National Bank, Úloha měnové politiky: Úloha měnové politiky ČNB podle zákona o ČNB [online]. Available at: (2018) [Google Scholar]
  12. J. A. Frankel, S. J. Wei, L. Goldberg, Assessing China’s exchange rate regime. Economic Policy, 22(51), 576-627, (2007) [CrossRef] [Google Scholar]
  13. E. Ogawa, M. Sakane, Chinese Yuan after Chinese Exchange Rate System Reform. China and World Economy, 14(6), 39-57, (2006) [Google Scholar]
  14. C. W. H. Cheong, J. Sinnakkannu, S. Ramasamy, On the predictability of carry trade returns: The case of the Chinese Yuan. Research in International Business and Finance, 39, 358-376, (2017) [CrossRef] [Google Scholar]
  15. S. Wang, X. Wei, Relationships between exchange rates, economic growth and FDI in China: An empirical study based on the TVP-VAR model. Littera Scripta, 10(1), 166-179, (2017) [Google Scholar]
  16. G. Ma, R. N Mccauley, The Implications of Renminbi Basket Management for Asian Currency Stability. The Evolving Role of Asia in Global Finance, 97-121, (2011) [CrossRef] [Google Scholar]
  17. Z. Zhang, K. Sato, Should Chinese Renminbi be Blamed for Its Trade Surplus? A Structural VAR Approach. The World Economy, 35(5), 632-650, (2012) [CrossRef] [Google Scholar]
  18. Z. Cai, L. Chen, Y. Fang, A New Forecasting Model for USD/CNY Exchange Rate. Studies in Nonlinear Dynamics and Econometrics, 16(3), (2012) [Google Scholar]
  19. Z. Liu, Z. Zheng, X. Liu, G. Wang, Modelling and Prediction of the CNY Exchange Rate Using RBF Neural Network. 2009 International Conference on Business Intelligence and Financial Engineering, 38-41, (2009) [CrossRef] [Google Scholar]
  20. World Bank. The World Bank. [online]. Available at: (2018) [Google Scholar]

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