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Review of Quantitative Finance and Accounting

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12-05-2017 | Original Research

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

Authors: Fathi Abid, Bilel Kaffel

Published in: Review of Quantitative Finance and Accounting | Issue 2/2018

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Abstract

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.

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ACF figure is available upon request.

ACF and PACF figures are available upon request.

We have used the GED and skewed -t distributions, but the results were not convincing.

The parameter estimates of diffusion stochastic processes are available upon request.

We have noticed that the best error interval is [0-1[. The error interval [1-2[is acceptable. When the errors belong to interval [2-3[, the simulated trajectories begin to gradually move away from the historical olive oil price curve. The bad interval is when the errors exceed 3. In this case, the simulated trajectories move quite away from the market ones.

Calculation details of the Kalman filter used to estimate the parameters of stochastic volatility model are available upon request.

The description of the DWT and MODWT is inspired from Percival and Walden (2000).

We have tried one input neuron corresponding to the lagged price change (lag 1) as well. The prediction results are the same as the model with two lagged price change in the input layer. Fitted trajectories and error histograms are available upon request.

The frequency histograms of this section are available upon request.

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Title: The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators
Authors: Fathi Abid
Bilel Kaffel
Publication date: 12-05-2017
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 2/2018
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-017-0638-9

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