IDEAS home Printed from https://ideas.repec.org/a/mth/ber888/v4y2014i2p49-62.html
   My bibliography  Save this article

Exchange Rate Model Approximation, Forecast and Sensitivity Analysis by Neural Networks, Case Of Iran

Author

Listed:
  • Dr. Mehdi Pedram
  • Maryam Ebrahimi

Abstract

This paper investigates the model estimation and data forecasting of exchange rate using artificial neural network. Recent studies have shown the classification and prediction power of the neural networks. It has been demonstrated that a neural network can approximate any continuous function. Here, in a technical approach, it has been used ARIMA and neural network for a short-term forecast of daily USD to Rial exchange rate. ANN is employed in training and learning processes and thereafter the forecast performance measured making use of two common loss functions. The comparison demonstrates that neural network is far better than ARIMA, the error is about the half. Thereafter, in a fundamental approach via another neural network the effects of some of the most important economic variables on exchange rate prediction in a long-term sense are studied. By sensitivity analysis, the importance and the weight of each economic variable on exchange rate has determined. The results show that it is possible to estimate a model to forecast the value of exchange rate even by having access to a limited subset of data.

Suggested Citation

  • Dr. Mehdi Pedram & Maryam Ebrahimi, 2014. "Exchange Rate Model Approximation, Forecast and Sensitivity Analysis by Neural Networks, Case Of Iran," Business and Economic Research, Macrothink Institute, vol. 4(2), pages 49-62, December.
    • Handle: RePEc:mth:ber888:v:4:y:2014:i:2:p:49-62
    as

    Download full text from publisher

    File URL: http://www.macrothink.org/journal/index.php/ber/article/view/5892/4906
    Download Restriction: no
    File URL: http://www.macrothink.org/journal/index.php/ber/article/view/5892
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guerard, John, 2023. "Harry Markowitz: An appreciation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1496-1501.
    2. Derick D. Quintino & Sergio A. David & Carlos E. de F. Vian, 2017. "Analysis of the Relationship between Ethanol Spot and Futures Prices in Brazil," IJFS, MDPI, vol. 5(2), pages 1-10, April.
    3. Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability Of Technical Trading Rules In Us Futures Markets: A Data Snooping Free Test," 2004 Conference, April 19-20, 2004, St. Louis, Missouri 19011, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    4. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    5. Mishra, Sasmita & Padhy, Sudarsan, 2019. "An efficient portfolio construction model using stock price predicted by support vector regression," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    6. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    7. Stefan Nagel, 2013. "Empirical Cross-Sectional Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 5(1), pages 167-199, November.
    8. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    9. De Santis, Paola & Drago, Carlo, 2014. "Asimmetria del rischio sistematico dei titoli immobiliari americani: nuove evidenze econometriche [Systematic Risk Asymmetry of the American Real Estate Securities: Some New Econometric Evidence]," MPRA Paper 59381, University Library of Munich, Germany.
    10. Nikolai Dokuchaev, 2015. "Modelling Possibility of Short-Term Forecasting of Market Parameters for Portfolio Selection," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 143-161, May.
    11. Sheriffdeen A. Tella & Olumuyiwa G. Yinusa & Ayinde Taofeek Olusola & Saban Celik, 2011. "Global Economic Crisis And Stock Markets Efficiency: Evidence From Selected Africa Countries," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 25(1), pages 139-169.
    12. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    13. Harry Mamaysky & Matthew Spiegel & Hong Zhang, 2007. "Improved Forecasting of Mutual Fund Alphas and Betas," Review of Finance, European Finance Association, vol. 11(3), pages 359-400.
    14. Remes, Piia, 2013. "Putting a Price on Carbon – Econometric Essays on the European Union Emissions Trading Scheme and its Impacts," Research Reports 62, VATT Institute for Economic Research.
    15. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    16. Ben Moews, 2023. "On random number generators and practical market efficiency," Papers 2305.17419, arXiv.org, revised Jul 2023.
    17. Mishra, Sasmita & Padhy, Sudarsan & Mishra, Satya Narayan & Misra, Satya Narayan, 2021. "A novel LASSO – TLBO – SVR hybrid model for an efficient portfolio construction," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    18. Xie Haibin & Zhou Mo & Yu Mei & Hu Yi, 2014. "Forecasting the Crude Oil Price with Extreme Values," Journal of Systems Science and Information, De Gruyter, vol. 2(3), pages 193-205, June.
    19. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
    20. Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45615, University Library of Munich, Germany.

    More about this item

    Keywords

    Exchange rate; Forecast; Model approximation; Crude oil; Gold; Price index; Sensitivity analysis.;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mth:ber888:v:4:y:2014:i:2:p:49-62. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Technical Support Office (email available below). General contact details of provider: http://www.macrothink.org/journal/index.php/ber .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.