On the predictability of energy commodity markets by an entropy-based computational method

F. Benedetto, G. Giunta and L. Mastroeni

Energy Economics, 2016, vol. 54, issue C, 302-312

Abstract: This paper proposes a novel computational method for assessing the predictability of commodity market time series, by predicting the entropy of the series under investigation. Assessing the predictability of a time series is the first mandatory step in order to further apply low-risk and efficient price forecasting methods. According to conventional entropy-based analysis (where the entropy is always ex-post estimated), high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict (i.e. ex-ante) the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm, usually used in many computational aspects of management science. Preliminary results, applied to energy commodity futures, show the effectiveness of the proposed method for application to energy market time series.

Keywords: Computational methods; Entropy analysis; Market efficiency; Energy commodity markets; Risk management science (search for similar items in EconPapers)
JEL-codes: C53 C63 G13 Q47 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:54:y:2016:i:c:p:302-312

DOI: 10.1016/j.eneco.2015.12.009

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