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

Mietek A. Brdyś; Marcin T. Brdyś; Sebastian M. Maciejewski

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 1, page 161-173
  • ISSN: 1641-876X

Abstract

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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-dayahead 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.

How to cite

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Mietek A. Brdyś, Marcin T. Brdyś, and Sebastian M. Maciejewski. "Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations." International Journal of Applied Mathematics and Computer Science 26.1 (2016): 161-173. <http://eudml.org/doc/276516>.

@article{MietekA2016,
abstract = {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-dayahead 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.},
author = {Mietek A. Brdyś, Marcin T. Brdyś, Sebastian M. Maciejewski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {currency exchange rate; artificial intelligence; state space wavelet network; Metropolis Monte Carlo; forecast combinations; data generating process},
language = {eng},
number = {1},
pages = {161-173},
title = {Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations},
url = {http://eudml.org/doc/276516},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Mietek A. Brdyś
AU - Marcin T. Brdyś
AU - Sebastian M. Maciejewski
TI - Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 1
SP - 161
EP - 173
AB - 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-dayahead 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.
LA - eng
KW - currency exchange rate; artificial intelligence; state space wavelet network; Metropolis Monte Carlo; forecast combinations; data generating process
UR - http://eudml.org/doc/276516
ER -

References

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