Journal Article10.1016/j.resourpol.2021.102544
Gold price forecasting using multivariate stochastic model
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TL;DR: In this paper , the authors used the Autoregressive Distribution Lag (ARDL) model to forecast annual gold prices using gold demand, treasury bills rates, and lagged gold prices as covariates.
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About: This article is published in Resources Policy. The article was published on 01 Jun 2022. The article focuses on the topics: Cointegration & Granger causality.
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A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices
TL;DR: In this article , a three-stage hybrid learning paradigm was proposed for predicting the prices of three precious metals, i.e., silver, palladium, and platinum, and the experimental results show that: the proposed novel learning paradigm achieves MAPE values of 0.2003, 0.4552, and 0.2151% for one-step-ahead forecasting of the prices, respectively; the proposed hybrid model performs the best among all the compared models, where hybridization of all the components is essential to improve the prediction accuracy and enable the proposed model to have a higher generalization capability.
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