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UAL Research Online

Forecasting the Price of Gold

Hassani, H. and Silva, E.S. and Gupta, R. and Segnon, M.K. (2015) Forecasting the Price of Gold. Applied Economics, 47 (39). pp. 4141-4152.

Type of Research: Article
Creators: Hassani, H. and Silva, E.S. and Gupta, R. and Segnon, M.K.

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases.

Additional Information (Publicly available):

This article is not Open Access due to the publisher's copyright restrictions. Please contact ualresearchonline to request a copy for personal use.

Keywords/subjects not otherwise listed: Gold, forecast, multivariate, univariate
Your affiliations with UAL: Colleges > London College of Fashion
Date: 2015
Digital Object Identifier: 10.1080/00036846.2015.1026580
Date Deposited: 28 Apr 2016 10:52
Last Modified: 27 Apr 2017 10:50
Item ID: 9177
URI: https://ualresearchonline.arts.ac.uk/id/eprint/9177

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