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The extent of virgin olive-oil prices' distribution revealing the behavior of market speculators

机译：原始橄榄油价格分布的程度揭示了市场投机者的行为

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摘要

The major problem facing olive oil producers each winter campaign, contrary to what is expected, is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. The aim of this paper is to study the olive oil price formation mechanisms in order to learn about the traders' behavior in the olive oil market. We econometrically study the price formation by implementing statistical models and we provide an economic explanation for the stylized facts detected in olive oil price series. For prediction purposes, we use the artificial neural network (ANN) approach. Our main findings indicate that the AR(1)-GJR(1,1) model and the Ornstein-Uhlenbeck process with stochastic volatility succeeded to some extent in capturing the series stylized facts. The unstable participants' behavior creates the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation ANN approach with input information based on discrete wavelet decomposition and recent price past history.
机译：与预期相反，橄榄油生产商在每个冬季运动中面临的主要问题不是收获是否良好，而是销售价格是否将使他们能够支付生产成本并获得合理的利润率。本文的目的是研究橄榄油价格形成机制，以了解交易者在橄榄油市场中的行为。我们通过实施统计模型计量研究价格形成，并对橄榄油价格系列中发现的风格化事实进行经济学解释。为了进行预测，我们使用人工神经网络（ANN）方法。我们的主要发现表明，具有随机波动性的AR（1）-GJR（1,1）模型和Ornstein-Uhlenbeck过程在一定程度上成功地捕获了一系列程式化的事实。不稳定参与者的行为会造成波动性聚集，非线性相关性和周期性现象。通过在活动的某些阶段互相模仿，不同的参与者会贡献出橄榄油价格分布中观察到的胖尾巴。橄榄油价格的最佳预测模型基于反向传播ANN方法，该方法具有基于离散小波分解和最近价格历史记录的输入信息。

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