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Forecasting Gold Price with Auto Regressive Integrated Moving Average Model

Author

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  • Naliniprava Tripathy

    (Indian Institute of Management Shillong, Shillong, Meghalaya, India.)

Abstract

The present study forecasts the gold price of India by using auto regressive integrated moving average (ARIMA) model over a period of 25 years from July 1990 to February 2015. The study also uses mean absolute error (MAE), root mean square error, maximum absolute percentage error, maximum absolute error (Max AE), and mean absolute percentage error (MAPE) to evaluate the accuracy of the model. The result of the study suggests that ARIMA (0,1,1) is the most suitable model used for forecasting the Indian gold prices since it contains least MAPE, Max AE and MAE. The study suggests that the past 1-month gold price has a significant impact on current gold price. The result of the study are particularly important to investors, economists, market regulators and policy makers for understanding the effectiveness of gold price to take better investment decision and devise better risk management tools

Suggested Citation

  • Naliniprava Tripathy, 2017. "Forecasting Gold Price with Auto Regressive Integrated Moving Average Model," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 324-329.
    • Handle: RePEc:eco:journ1:2017-04-41
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    References listed on IDEAS

    as
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    Cited by:

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    2. Quarm, Richmond Sam & Busharads, Mohamed Osman Elamin & Institute of Research, Asian, 2020. "Modeling and Forecasting Gold Prices," OSF Preprints u5mz6, Center for Open Science.
    3. Mutele, Litshedzani & Carranza, Emmanuel John M., 2024. "Statistical analysis of gold production in South Africa using ARIMA, VAR and ARNN modelling techniques: Extrapolating future gold production, Resources–Reserves depletion, and Implication on South Afr," Resources Policy, Elsevier, vol. 93(C).
    4. Khan, Asad Ul Islam & Shahbaz, Muhammad & Napari, Ayuba, 2023. "Subsample stability, change detection and dynamics of oil and metal markets: A recursive approach," Resources Policy, Elsevier, vol. 83(C).
    5. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2022. "Gold price forecasting using multivariate stochastic model," Resources Policy, Elsevier, vol. 76(C).

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    More about this item

    Keywords

    Auto Regressive Integrated Moving Average; Gold Price; Forecasting Techniques; Multiple Regression;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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