Measuring the competitiveness of commodity markets using price signals and information theory

Anis  Hoayek; Hassan Hamie

doi:10.31181/dmame622023548

Authors

Anis Hoayek Institut Henri Fayol, École nationale supérieure des mines de Saint-Étienne, Saint-Etienne, France https://orcid.org/0000-0002-6499-6447
Hassan Hamie Energy Economics Group, Vienna University of Technology, Vienna, Austria https://orcid.org/0000-0003-3939-277X

DOI:

https://doi.org/10.31181/dmame622023548

Keywords:

Markets efficiency, price signals, information theory, commodity markets

Abstract

Technological advancements, abrupt changes in market conditions, and political reforms, among other things, necessitate strong regulatory oversight, and accurate measurement of performance related indicators. The more accurate, information rich, and transparent these measurements/signals, the lower the level of uncertainty felt by value chain participants, who are thus able to recognize and observe whether the market’s state is efficient. Its lack, may lead to indecisiveness, translating into false interpretations that could lead to wrong policy directions. This paper provides an ex-post evaluation tool intending to deliver additional insights or quality information that would aid the regulator in assessing the state of the market. The tool is applied to the UK wholesale natural gas market for the period between 2011 and 2020, assessing and testing the market’s weak-form efficiency. It claims that today’s gas prices reflect a specific type of information, primarily past gas prices, and that only new information can help predict future prices. In this manuscript, based solely on a limited and available untapped dataset (day-ahead price time series), and working under the assumption that gas prices are the result of market processes, a variety of information metrics (gas price randomness, distribution of extreme prices, ability to predict prices - based on historical sets) is extracted with the use of suitable mathematical statistical models. A weighted entropy index is then computed, and measures the state of the commodity market. The results indicate that the analysis has helped gain information, thus reducing uncertainty (relative to a pre-analysis) by 86.5 %. Additionally, there is sufficient evidence that the UK natural gas prices are weak-form efficient.

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References

Akcora, B., & Kocaaslan, O. K. (2023). Price bubbles in the European natural gas market between 2011 and 2020. Resources Policy, 80, 103-136.

Arnold, B. C., Balakrishnan, N., & Nagaraja, H. N. (1998). Records. John Wiley & Sons. New York. DOI: https://doi.org/10.1002/9781118150412

Bohl, M. T., Pütz, A., & Sulewski, C. (2021). Speculation and the informational efficiency of commodity futures markets. Journal of Commodity Markets, 23, 100-129.

Broadstock, D. C., Li, R., & Wang, L. (2020). Integration reforms in the European natural gas market: A rolling-window spillover analysis. Energy Economics, 92, 104-139.

Chen, Y., Wang, C., & Zhu, Z. (2022). Toward the integration of European gas futures market under COVID-19 shock: A quantile connectedness approach. Energy Economics, 114, 106-128.

Claesen, M., & De Moor, B. (2015). Hyperparameter search in machine learning. ArXiv Preprint ArXiv:1502.02127.

Franek, J., & Kresta, A. (2014). Judgment scales and consistency measure in AHP. Procedia Economics and Finance, 12, 164–173. DOI: https://doi.org/10.1016/S2212-5671(14)00332-3

Hamie, H., Hoayek, A., & Auer, H. (2018). Modeling the price dynamics of three different gas markets-records theory. Energy Strategy Reviews, 21, 121–129.

Hamie, H., Hoayek, A., Kamel, M., & Auer, H. (2020). Northwestern European wholesale natural gas prices: Comparison of several parametric and non-parametric forecasting methods. International Journal of Global Energy Issues, 42(3–4), 259–284.

Heather, P. (2010). The Evolution and Functioning of the Traded Gas Market in Britain. OIES Paper NG44. DOI: https://doi.org/10.26889/9781907555152

Heather, P. (2012). Continental European Gas Hubs: are they fit for purpose? OIES Paper NG63. DOI: https://doi.org/10.26889/9781907555510

Heather, P. (2019). European traded gas hubs: a decade of change. OIES Insight OEI55.

Helm, D., & Jenkinson, T. (1997). The assessment: Introducing competition into regulated industries. Oxford Review of Economic Policy. doi:10.1093/oxrep/13.1.1. DOI: https://doi.org/10.1093/oxrep/13.1.1

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI: https://doi.org/10.1162/neco.1997.9.8.1735

Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. DOI: https://doi.org/10.1162/003465303772815961

Lesne, A. (2014). Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics. Mathematical Structures in Computer Science, 24(3), e240311. doi:10.1017/S0960129512000783. DOI: https://doi.org/10.1017/S0960129512000783

Lindström, E., & Regland, F. (2012). Modeling extreme dependence between European electricity markets. Energy Economics, 34(4), 899–904. DOI: https://doi.org/10.1016/j.eneco.2012.04.006

Martínez, B., & Torró, H. (2023). Theory of storage implications in the European natural gas market. Journal of Commodity Markets, 8(1), 100-121.

Meucci, A. (2011). ‘The Prayer’Ten-Step Checklist for Advanced Risk and Portfolio Management. Ten-Step Checklist for Advanced Risk and Portfolio Management. doi: 10.2139/ssrn.1753788. DOI: https://doi.org/10.2139/ssrn.1753788

Mishra, V., & Smyth, R. (2016). Are natural gas spot and futures prices predictable? Economic Modelling, 54, 178–186. DOI: https://doi.org/10.1016/j.econmod.2015.12.034

Narayan, P. K., & Popp, S. (2010). A new unit root test with two structural breaks in level and slope at unknown time. Journal of Applied Statistics, 37(9), 1425–1438. DOI: https://doi.org/10.1080/02664760903039883

Nick, S. (2016). The informational efficiency of European natural gas hubs: Price formation and intertemporal arbitrage. The Energy Journal, 37(2). 25-42. doi: 10.5547/01956574.37.2.snic. DOI: https://doi.org/10.5547/01956574.37.2.snic

Pachón-Suescún, C. G., Pinzón-Arenas, J. O., & Jiménez-Moreno, R. (2020). Abnormal gait detection by means of LSTM. International Journal of Electrical & Computer Engineering (2088-8708), 10(2), 12-26.

Papież, M., Rubaszek Michałand Szafranek, K., & Śmiech, S. (2022). Are European natural gas markets connected? A time-varying spillovers analysis. Resources Policy, 79, 103-129.

Saaty, T. L. (2004). Fundamentals of the analytic network process—multiple networks with benefits, costs, opportunities and risks. Journal of Systems Science and Systems Engineering, 13(3), 348–379. DOI: https://doi.org/10.1007/s11518-006-0171-1

Uribe, J. M., Mosquera-López, S., & Arenas, O. J. (2022). Assessing the relationship between electricity and natural gas prices in European markets in times of distress. Energy Policy, 166, 113-128.