Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations

Brdyś, M. A. ; Brdyś, M. T. ; Maciejewski, S. M.

International Journal of Applied Mathematics and Computer Science, Tome 26 (2016) no. 1, pp. 161-173.

Résumé

The paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.

Détail

Keywords: currency exchange rate, artificial intelligence, state space wavelet network, Metropolis Monte Carlo, forecast combinations, data generating process
Mots-clés : kurs wymiany walut, sztuczna inteligencja, sieć falkowa

Bibliographie

[1] BIS (2013). Foreign Exchange Turnover in April 2013: Preliminary Global Results, Triennial Central Bank Survey, Bank of International Settlements, Basel.

[2] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31(3): 307–327.

[3] Borowa, A., Brdyś, M.A. and Mazur, K. (2007). Modelling of wastewater treatment plant for monitoring and control purposes by state-space wavelet networks, International Journal of Computers, Communications Control 2(2): 121–131.

[4] Brdyś, M.A., Borowa, A., Idźkowiak, P. and Brdyś, M.T. (2009). Adaptive predictions of stock exchange indices state space wavelet networks, International Journal of Applied Mathematics and Computer Science 19(2): 337–348, DOI: 10.2478/v10006-009-0029-z.

[5] CSO (2014). Foreign Trade in January–December 2013, Statistical Information and Elaborations, Central Statistical Office, Warsaw.

[6] Grossmann, A. and Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape, SIAM Journal on Mathematical Analysis 15(4): 723–736.

[7] Guidolin, M. and Timmermann, A. (2009). Forecasts of US short-term interest rates: A flexible forecast combination approach, Journal of Econometrics 150(2): 297–311.

[8] Hajek, B. (1988). Cooling schedules for optimal annealing, Mathematics of Operations Research 13(2): 311–329.

[9] Hyndman, R.J., Ahmed, R.A., Athanasopoulos, G. and Shang, H.L. (2011). Optimal combination forecasts for hierarchical time series, Computational Statistics and Data Analysis 55(9): 2579–2589.

[10] Jacobson, S.H., Hall, S.N., Mclay, L.A. and Orosz, J.E. (2005). Performance analysis of cyclical simulated annealing algorithms, Methodology and Computing in Applied Probability 7(2): 183–201.

[11] Karafyllidis, I. (1999). A simulator for single-electron tunnel devices and circuits based on simulated annealing, Superlattices and Microstructures 25(4): 567–572.

[12] Kawakami, K. (2013). Conditional forecast selection from many forecasts: An application to the yen/dollar exchange rate, Journal of the Japanese and International Economies 28(C): 1–18.

[13] Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983). Optimization by simulated annealing, Science 220(4598): 671–680.

[14] Kulawski, G.J. and Brdyś, M.A. (2000). Stable adaptive control with recurrent networks, Automatica 36(1): 5–22.

[15] Kuo, R.J., Chen, C.H. and Hwang, Y.C. (2001). An intelligent stock trading support system through integration of genetic algorithm based fuzzy neural network and artificial neural network, Fuzzy Sets and Systems 118(1): 21–45.

[16] Locatelli, M. (2000). Convergence and first hitting time of simulated annealing algorithms for continuous global optimization, Mathematical Methods of Operations Research 54(2): 171–199.

[17] McCracken, M.W. and Clark, T.E. (2009). Improving forecast accuracy by combining recursive and rolling forecasts, International Economic Review 50(2): 363–395.

[18] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953). Equations of state calculations by fast computing machines, Journal of Chemical Physics 21: 1087–1092.

[19] NBP (2013). Turnover in the Domestic Foreign Exchange and OTC Derivatives Markets in April 2013, National Bank of Poland, Warsaw.

[20] Pesaran, M.H. and Pick, A. (2011). Forecast combination across estimation windows, Journal of Business Economics and Statistics 29(2): 307–318.

[21] Qi, R. and Brdyś, M.A. (2008). Stable indirect adaptive control based on discrete-time T–S fuzzy model, Fuzzy Sets and Systems 159(8): 900–925.

[22] Qi, R. and Brdyś, M.A. (2009). Indirect adaptive control based on a self-structuring fuzzy system for nonlinear modelling and control, International Journal of Applied Mathematics and Computer Science 19(4): 619–630, DOI: 10.2478/v10006-009-0049-8.

[23] Rapach, D.E., Strauss, J.K. and Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy, The Review of Financial Studies 23(2): 821–862.

[24] Stock, J.H. and Watson, M.W. (2004). Combination forecasts of output growth in a seven-country data set, Journal of Forecasting 23(6): 405–430.

[25] Tian, J. and Anderson, H.M. (2014). Forecast combinations under structural break uncertainty, International Journal of Forecasting 30(1): 161–175.

[26] Timmermann, A. (2006). Forecast combinations, in G. Elliott et al. (Eds.), Handbook of Economic Forecasting, Elsevier, Amsterdam.

[27] Tsang, P.M., Kwok, P., Choy, S.O., Kwan, R., Ng, S.C., Mak, J., Tsang, J., Koong, K. and Wong, T. (2007). Design and implementation of NN5 for Hong Kong stock price forecasting, Engineering Applications of Artificial Intelligence 20(4): 453–461.

[28] Zammarreno, J.M. and Pastora, V. (1998). State space neural network: Properties and applications, Neural Networks 11(6): 1099–1112.

[29] Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998). Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting 14(1): 35–62.

[30] Zhang, Q. (1992). Wavelet network: The radial structure and an efficient initialization procedure, Technical Report LiTHISY-I-1423, Linkoping University, Linkoping.

[31] Zhang, Q. and Beneveniste, A. (1992). Wavelet networks, IEEE Transactions on Neural Networks 3(6): 889–898.