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dc.contributor.author | Hassani, Hossein![]() |
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dc.contributor.author | Silva, Emmanuel Sirimal![]() |
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dc.contributor.author | Gupta, Rangan![]() |
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dc.contributor.author | Segnon, Mawuli K.![]() |
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dc.date.accessioned | 2015-05-29T12:16:05Z | |
dc.date.available | 2015-05-29T12:16:05Z | |
dc.date.issued | 2015-03 | |
dc.description.abstract | 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 smooth-ing 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. | en_ZA |
dc.description.embargo | 2016-09-26 | en_ZA |
dc.description.librarian | hb2015 | en_ZA |
dc.description.uri | http://www.tandfonline.com/loi/raec20 | en_ZA |
dc.identifier.citation | Hassani, H, Silva, ES, Gupta, R & Segnon, MK 2015, 'Forecasting the price of gold', Applied Economics, vol. 47, no. 39, pp. 4141-4152. | en_ZA |
dc.identifier.issn | 0003-6846 (print) | |
dc.identifier.issn | 1466-4283 (online) | |
dc.identifier.other | 10.1080/00036846.2015.1026580 | |
dc.identifier.uri | http://hdl.handle.net/2263/45362 | |
dc.language.iso | en | en_ZA |
dc.publisher | Routledge | en_ZA |
dc.rights | © Taylor and Francis. This is an electronic version of an article published in Applied Economics, vol. 47, no. 39, pp. 4141-4152, 2015. doi : 10.1080/00036846.2015.1026580. Applied Economics is available online at : http://www.tandfonline.comloi/raec20 | en_ZA |
dc.subject | Gold | en_ZA |
dc.subject | Forecast | en_ZA |
dc.subject | Multivariate | en_ZA |
dc.subject | Univariate | en_ZA |
dc.subject | Autoregressive integrated moving average (ARIMA) | en_ZA |
dc.subject | Exponential smoothing (ETS) | en_ZA |
dc.subject | ARIMA model (ARFIMA) | en_ZA |
dc.subject | Trend and seasonal components (TBATS) | en_ZA |
dc.subject | Vector autoregression (VAR) | en_ZA |
dc.subject | Bayesian autoregression (BAR) | en_ZA |
dc.subject | Bayesian VAR (BVAR) | en_ZA |
dc.subject | Random walk (RW) | en_ZA |
dc.subject | Autoregression (AR) | en_ZA |
dc.title | Forecasting the price of gold | en_ZA |
dc.type | Postprint Article | en_ZA |