Cyclical non-stationarity in commodity prices

Øglend, Atle; Asche, Frank

doi:10.1007/s00181-015-1060-6

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…First, an essential characteristic of the wavelet method is to capture latent processes with time varying cycle trends, lead-lag interactions, and patterns in the underlying time series ( Fakhfekh et al, 2021 ; Sherif, 2020 ). Second, a major advantage of wavelet coherence approach relates with its flexibility in dealing with non-stationary signals which are prevalent in the prices of soft commodities ( Aguiar-Conraria and Soares, 2011 ; Demir et al, 2020 ; Jiang and Yoon, 2020 ; León and Soto, 1997 ; Oglend and Asche, 2016 ; Shehzad et al, 2021 ). In addition, the wavelet coherence framework is also useful to study the co-movements between time series variables with frequent structural changes ( Fruehwirt et al, 2021 ; Kristoufek et al, 2016 ), and the variables that face sudden fluctuations in their structure.…”

Section: Data and Research Methodologymentioning

confidence: 99%

Safe-haven properties of soft commodities during times of Covid-19

Rubbaniy

Khalid

Syriopoulos

et al. 2022

Journal of Commodity Markets

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We use wavelet coherence analysis on global COVID-19 fear index and, soft commodities’ spot and futures prices to investigate safe-haven properties of soft commodities over the period from January 28, 2020 to April 29, 2021. Our findings show that each of the sampled soft commodities shows safe-haven behavior in one of the spot or futures markets and for one of the short-term or long-term investors during the times of COVID-19. Our results also show that safe-haven properties of soft commodities are contingent upon the nature of the commodity. The findings of our mean-variance portfolio analysis indicate that the portfolios with commodity futures are less risky and efficient compared to the portfolio containing stocks only, thus robustly supporting the safe-haven properties of soft commodities during COVID-19. Our results not only have important implications for individual investors and asset managers in suggesting particular soft commodities to strengthen safe-haven and diversification features of their portfolios but also can assist the policy makers to understand and disentangle health fear dimension of several interlocking dynamics affecting the spot and futures prices of soft commodities during COVID-19.

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Section: Data and Research Methodologymentioning

confidence: 99%

Safe-haven properties of soft commodities during times of Covid-19

Rubbaniy

Khalid

Syriopoulos

et al. 2022

Journal of Commodity Markets

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“…Copper price is determined by the supply and demand dynamics on the metal exchanges, especially the London Metal Exchange. Although it may be strongly influenced by the currency exchange rate and the investment flow, the factors that can cause fluctuations in volatile prices are partially associated with changes in the activity of the economic cycle [1].…”

Section: Introductionmentioning

confidence: 99%

Copper Price Prediction Using Support Vector Regression Technique

et al. 2020

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Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε, γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.

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Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting

Kim,

Ham,

et al. 2024

Port Econ J

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Cyclical non-stationarity in commodity prices

Cited by 6 publications

References 34 publications

Safe-haven properties of soft commodities during times of Covid-19

Safe-haven properties of soft commodities during times of Covid-19

Copper Price Prediction Using Support Vector Regression Technique

Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting

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