Economics and Business
ISSN 2775-9237 (Online)
Published: 28 December 2020
Modeling and Forecasting Gold Prices
Latifa Ghalayini, Sara Farhat
Lebanese University, Lebanon
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Keywords: Dynamic OLS, Exchange Rate, Gold Future Market, Gold Price, Oil Price, Open Interest
The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.
Abdullah, L. (2012). ARIMA Model for Gold Bullion Coin Selling Prices Forecasting. International Journal of Advances in Applied Sciences, 1(4), 153-158. https://doi.org/10.11591/ijaas.v1i4.1495
Apergis, N. (2014). Can gold prices forecast the Australian dollar movements? International Review of Economics and Finance, 29, 75-82. https://doi.org/10.1016/j.iref.2013.04.004
Baker, S. A., & Van Tassel, R. C. (1985). Forecasting the Price of Gold:A Fundamentalist Approach. Atlantic Economic Journal, 13(4), 43–51. https://doi.org/10.1007/BF02304036
Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking & Finance, 34(8), 1886–1898. https://doi.org/10.1016/j.jbankfin.2009.12.008
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
Cai, J., Cheung, Y.-L., & Wong, M. C. (2001). What Moves the Gold Market. The Journal of Futures Markets, 21, 257–278. https://doi.org/10.1002/1096-9934(200103)21:3<257::AID-FUT4>3.0.CO;2-W
Davis, R., Dedu, V. K., & Bonye, F. (2014). Modeling and Forecasting of Gold Prices on Financial Markets. American International Journal of Contemporary Research, 4, 107-113.
Engle, R. F., & Granger, C. (1987). Co-integration and error-correction: representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236
Granger, C. W. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. https://doi.org/10.2307/1912791
Guha, B., & Bandyopadhyay, G. (2016). Gold Price Forecasting Using ARIMA Model. Journal of Advanced Management Science, 4(2), 117-121.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12 (2-3), 231-254. https://doi.org/10.1016/0165-1889(88)90041-3
Khan, M. M. (2013). Forecasting of Gold Prices (Box Jenkins Approach). International Journal of Emerging Technology and Advanced Engineering, 3(3), 662-670.
Lawrence, C. (2003). Why is gold different from other assets? World Gold Council.
Levin, E. J., & Wright, R. E. (2006). Short-run and Long-run Determinants of the Price of Gold.World Gold Council. Research Study No. 32.
Saikkonen, P. (1991). Asymptotically efficient estimation of cointegration regressions. Econometric Theory, 7(1), 1-21. https://doi.org/10.1017/S0266466600004217
Šimáková, J. (2011). Analysis of the Relationship between Oil and Gold Prices. The Journal of Finance, 51, 651-662.
Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica, 61(4), 783-820. https://doi.org/10.2307/2951763
Tripathy, N. (2017). Forecasting Gold Price with Auto Regressive Integrated Moving Average Model. International Journal of Economics and Financial Issues, 7, 324-329.
Tully, E., & Lucey, B. M. (2007). A power GARCH examination of the gold market. Research in International Business and Finance, 21(2), 316–325. https://doi.org/10.1016/j.ribaf.2006.07.001
World Gold Council. (2019). Available online: