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

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.