Page 48 - Contributed Paper Session (CPS) - Volume 8
P. 48

CPS2176 Chiraz KARAMTI et al.
                  expected, wavelets provide a degree of refinement and flexibility not available
                  using conventional forecasting methods. With wavelets, one can choose the
                  scale at which the forecast is to be made. As evidenced by our results, each
                  scale level has to be treated as a separate series for forecasting purposes.
                  These  findings  suggest  that  forecasting  is  more  delicate  than  has  been
                  recognized so far and that forecasts need to be expressed conditional on the
                  relevant scales (Gallegati and Semmler, 2014; Yousefi et al., 2005).

                  References
                  1.  Bank for International Settlements, Real Broad Effective Exchange Rate
                      for Euro Area [RBXMBIS], retrieved from FRED, Federal Reserve Bank of
                      St. Louis; https://fred.stlouisfed.org/series/RBXMBIS, January 30, 2019.
                  2.  Gallegati, M. and Semmler, W. (eds.), Wavelet Applications in Economics
                      and Finance, Dynamic Modeling and Econometrics in Economics and
                      Finance 20, (2014) DOI 10.1007/978-3-319-07061-2__1. Springer
                      International Publishing Switzerland.
                  3.  Gallegati, M., Ramsey, JB., Semmler, W. (2013). Time scale analysis of
                      interest rate spreads and output using wavelets. Axioms 2:182–207.
                  4.  He, K., Xie, C., Chen, S., Lai, K.K., (2009). Estimating var in crude oil
                      market: A novel multi-scale non-linear ensemble approach incorporating
                      wavelet analysis and neural network. Neurocomputing, 72, 3428- 3438.
                  5.  Ismail, M.T., Audu, B., Tumala, M.M. (2016). Volatility forecasting with the
                      wavelet transformation algorithm GARCH model: Evidence from African
                      stock markets. The Journal of Finance and Data Science, 2(2), 125-135.
                  6.  Jammazi, R., 2012. Oil shock transmission to stock market returns:
                      Wavelet-multivariate markov switching garch approach. Energy, 37, 430-
                      454.
                  7.  Mallat. S., (2001). A Wavelet Tour of Signal. Processing. Academic Press,
                      San Diego.
                  8.  Marchand-Blanchet, F. (1998). Une approche de la compétitivité de la
                      zone euro: le taux de change effectif de l’euro, Bulletin De La Banque De
                      France, n°60, 103.
                  9.  Masih, M., Alzahrani, M., Al Titi, O., (2010). Systematic risk and time
                      scales: New evidence from an application of wavelet approach to the
                      emerging Gulf stock markets. International Review of Financial Analysis,
                      19, 10-18.
                  10.  Rua, A., Nunes, L.C., (2009). International comovement of stock market
                      returns: A wavelet analysis. Journal of Empirical Finance, 16, 632-639.
                  11.  Schmitz, M., De Clercq, M., Fidora, M., Lauro B., and Pinheiro, C. (2012).
                      Revisiting The Effective Exchange Rates of The Euro. European Central
                      Bank Occasional Paper series, No 134, June 2012.



                                                                      37 | I S I   W S C   2 0 1 9
   43   44   45   46   47   48   49   50   51   52   53