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Commodity Prices after COVID-19: Persistence and Time Trends

Author

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  • Manuel Monge

    (Faculty of Law, Business and Government, Universidad Francisco de Vitoria, E-28223 Madrid, Spain
    Current Address: Department of Financial Economics, Universidad Francisco de Vitoria, Crta. Pozuelo-Majadahonda, Km. 1800, Pozuelo de Alarcón, E-28223 Madrid, Spain.)

  • Ana Lazcano

    (Faculty of Law, Business and Government, Universidad Francisco de Vitoria, E-28223 Madrid, Spain
    Departamento de Ingeniería de Sistemas y Control, Universidad Nacional de Educación a Distancia (UNED), E-28040 Madrid, Spain
    Current Address: Departamento de Ingeniería de Sistemas y Control, ETSI Informática, UNED, C/Juan del Rosal, 16, E-28040 Madrid, Spain.)

Abstract

Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode.

Suggested Citation

  • Manuel Monge & Ana Lazcano, 2022. "Commodity Prices after COVID-19: Persistence and Time Trends," Risks, MDPI, vol. 10(6), pages 1-20, June.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:6:p:128-:d:840676
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    References listed on IDEAS

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    Cited by:

    1. Ana Lazcano & Pedro Javier Herrera & Manuel Monge, 2023. "A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting," Mathematics, MDPI, vol. 11(1), pages 1-21, January.
    2. Jesús Tomás Monge Moreno & Manuel Monge, 2023. "Coronavirus, Vaccination and the Reaction of Consumer Sentiment in The United States: Time Trends and Persistence Analysis," Mathematics, MDPI, vol. 11(8), pages 1-8, April.

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