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Trends and persistence in global olive oil prices after COVID-19

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  • Manuel Monge

    (Universidad Francisco de Vitoria)

Abstract

Once the coronavirus pandemic was declared by government authorities in March 2020 and several measures were adopted around the world to limit the effects of COVID-19, the limit agroeconomic processing affected important operations such as not being able to prepare the olive trees for the next harvest. This lack of processes has caused the consumer to perceive an increase in prices due to the shortage of product and the growing demand for olive oil around the world. This research paper, through the use of advanced statistical and econometric techniques, attempts to perform a specific analysis and understand the persistence of the data and the trend of global olive oil prices. Artificial intelligence techniques such as neural network models are also used to predict long-term price behavior. Using ARFIMA (p, d, q) model, the results suggest a non-mean reversion behavior, suggesting that the shock is expected to be permanent, causing a change in trend. This result is in line with that obtained using machine learning techniques, where the forecast suggests an increase of the prices around + 11.36% in the next 12 months.

Suggested Citation

  • Manuel Monge, 2024. "Trends and persistence in global olive oil prices after COVID-19," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 481-488, October.
    • Handle: RePEc:pal:jorapm:v:23:y:2024:i:5:d:10.1057_s41272-024-00481-x
    DOI: 10.1057/s41272-024-00481-x
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    Cited by:

    1. Ian Yeoman, 2024. "Using revenue management and pricing beyond the airline and hotel industries: an ever increasing pathway of success," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 381-383, October.

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    More about this item

    Keywords

    Global olive oil prices; Fractional integration; ARFIMA (p; d; q) model; Machine learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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