Forecasting the price of gold

Hossein Hassani · Emmanuel Sirimal Silva · Rangan Gupta

79 citations0/0 files storedApplied Economics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Financial Risk and Volatility Modeling Stock Market Forecasting Methods

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 79 citations exist upstream.

References

1980Macroeconomics and Reality 1992Testing the null hypothesis of stationarity against the alternative of a unit root 1991Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models n.d.Forecasting principles and practice 2008Automatic Time Series Forecasting: The forecast Package for R 1990Inference in Linear Time Series Models with some Unit Roots 1993Forecasting methods and applications 1997Testing the equality of prediction mean squared errors

Cited by

2021Predicting Gold and Silver Price Direction Using Tree-Based Classifiers 2021Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks 2017Predicting Future Gold Rates using Machine Learning Approach 2018Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns 2014Heterogeneous expectations in the gold market: Specification and estimation

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.