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A Correcting Note on Forecasting Conditional Variance Using ARIMA vs. GARCH Model

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  • Mohammad Naim Azimi
  • Seyed Farhad Shahidzada

Abstract

In this study, we demonstrate that a common approach in using the Autoregressive Integrated Moving Average model is not efficient to forecast all types of time series data and most specially, the out-of-sample forecasting of the time series that exhibits clustering volatility. This gap leads to introduce a competing model to catch up with the clustering volatility and conditional variance for which, we empirically document the efficient and lower error use of the Generalized Autoregressive Conditional Heteroscedasticity model instead.

Suggested Citation

  • Mohammad Naim Azimi & Seyed Farhad Shahidzada, 2019. "A Correcting Note on Forecasting Conditional Variance Using ARIMA vs. GARCH Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(5), pages 145-145, May.
    • Handle: RePEc:ibn:ijefaa:v:11:y:2019:i:5:p:145
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    References listed on IDEAS

    as
    1. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, vol. 76(1), pages 59-64, June.
    2. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091, October.
    3. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
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    JEL classification:

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

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