Forecasting the price of gold
Authors: Hassani, H., Silva, E.S., Gupta, R. and Segnon, M.K.
Journal: Applied Economics
Volume: 47
Issue: 39
Pages: 4141-4152
eISSN: 1466-4283
ISSN: 0003-6846
DOI: 10.1080/00036846.2015.1026580
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 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.
https://eprints.bournemouth.ac.uk/21799/
Source: Scopus
Forecasting the price of gold
Authors: Hassani, H., Silva, E.S., Gupta, R. and Segnon, M.K.
Journal: APPLIED ECONOMICS
Volume: 47
Issue: 39
Pages: 4141-4152
eISSN: 1466-4283
ISSN: 0003-6846
DOI: 10.1080/00036846.2015.1026580
https://eprints.bournemouth.ac.uk/21799/
Source: Web of Science (Lite)
Forecasting the price of gold
Authors: Hassani, H., Silva, E.S., Gupta, R. and Segnon, M.K.
Journal: Applied Economics
Volume: 47
Issue: 39
Pages: 4141-4152
Publisher: Routledge
eISSN: 1466-4283
ISSN: 0003-6846
DOI: 10.1080/00036846.2015.1026580
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 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.
https://eprints.bournemouth.ac.uk/21799/
Source: Manual
Preferred by: Emmanuel Sirimal Silva
Forecasting the price of gold
Authors: Hassani, H., Silva, E., Gupta, R. and Segnon, M.K.
Journal: Applied Economics
Volume: 47
Issue: 39
Pages: 4141-4152
ISSN: 0003-6846
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 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.
https://eprints.bournemouth.ac.uk/21799/
Source: BURO EPrints