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The Comparative Comparison of Exchange Rate Models

Author

Listed:
  • Kamran Mahmodpour

    (Department of Economics, University of Sistan and Baluchestan, Zahedan, Iran)

  • Yaser Sistani Badooei

    (Department of Economics, Baft Higher Education Center, Shahid Bahonar University of Kerman, Kerman, Iran)

  • Hadiseh Mohseni

    (Departments of Sama, Shirvan Branch, Islamic Azad University, Shirvan, Iran)

  • Saman Veismoradi

    (University of Sistan and Baluchestan, Zahedan, Iran)

Abstract

One of the most important and effectiveness of macroeconomics variables is prediction of future exchange rate trend which heavily considered by economic scholars. Its changes affects different parts of economic, thus it is necessary to model it to provide more suitable economic advising. In order to do that, in this paper we have used seasonal autoregressive integrated moving average (SARIMA), autoregressive conditional heteroskedastistiy (ARCH) and generalized ARCH (GARCH) models to simulate the time series trends of exchange rate in Iranian non-official market. The results show that GARCH provides better and more acceptable outputs than SARIMA.

Suggested Citation

  • Kamran Mahmodpour & Yaser Sistani Badooei & Hadiseh Mohseni & Saman Veismoradi, 2016. "The Comparative Comparison of Exchange Rate Models," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 380-385.
    • Handle: RePEc:eco:journ1:2016-02-2
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    References listed on IDEAS

    as
    1. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
    2. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
    3. Askari, Hossein & Krichene, Noureddine, 2008. "Oil price dynamics (2002-2006)," Energy Economics, Elsevier, vol. 30(5), pages 2134-2153, September.
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    Cited by:

    1. Takumi Ito & Motoki Masuda & Ayaka Naito & Fumiko Takeda, 2021. "Application of Google Trends‐based sentiment index in exchange rate prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1154-1178, November.

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    More about this item

    Keywords

    Seasonal Autoregressive Integrated Moving Average; Autoregressive Conditional Heteroskedastistiy; Generalized Autoregressive Conditional Heteroskedastistiy; Exchange Rate;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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