Do data artefacts manufacture climate and ESG risk signals in energy markets? Evidence from contract rolls in JKM LNG futures
This paper demonstrates that data artefacts arising from contract rolls in JKM LNG futures create artificial, calendar-driven price jumps that significantly distort risk signals, volatility estimates, and market linkage analyses, necessitating a specific diagnostic correction to ensure accurate energy market modeling.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the vast, churning world of global energy, a single number often dictates the cost of keeping homes warm and factories running across Asia. This number is the price of a cargo of liquefied natural gas, a fuel that has been chilled into a liquid state so it can be shipped across oceans. To track this price, traders and researchers rely on a specific benchmark known as the JKM, which acts as a daily thermometer for the Asian market. However, the data used to track this price is not a simple, continuous line of history. It is a chain of separate contracts, each covering a specific delivery month, stitched together end-to-end. As one contract expires and is replaced by the next, the price recorded for that day often jumps up or down. This jump is not a reaction to a storm, a shortage, or a change in demand; it is merely the mechanical result of switching from one piece of paper to another. For years, researchers have treated these jumps as real market movements, using them to calculate risk, predict climate impacts, and decide how much money banks must keep in reserve.
A new study by Arthur Walter Chen at Monash University reveals that this common practice has been building a false picture of reality. The researcher examined the daily price history of the JKM benchmark from late 2017 through 2025 and discovered that the data contains a hidden, repeating pattern that has nothing to do with the actual gas market. Because natural gas is heavily used for heating in winter and cooling in summer, the price of a winter delivery is naturally much higher than a summer delivery. When the data chain switches from a winter contract to a spring one, the price drops sharply. When it switches from summer to winter, it spikes. These switches happen on a fixed calendar schedule, creating a predictable sawtooth pattern in the data that looks like a massive market event but is actually just an accounting artifact. The study found that these few switching days, which make up less than five percent of all trading days, were responsible for nearly two-thirds of the apparent volatility in the price series. In other words, the market looked wildly unstable and dangerous, but much of that instability was an illusion created by the way the data was put together.
The researcher tested this idea by comparing the Asian gas data with similar data for European gas and crude oil from the same source. Unlike the Asian gas series, the European and oil data did not show these massive, calendar-driven jumps. This confirmed that the problem was specific to how the Asian gas contracts were chained together, not a general flaw in the data provider. The study then asked a critical question: do the standard methods researchers use to clean up messy data fix this problem? The answer was a definitive no. Common techniques, such as removing extreme outliers or smoothing the data, failed to eliminate the issue. This is because the "outliers" were not random errors; they were a systematic, repeating feature of the data chain. When researchers tried to smooth them out, the artificial spikes remained, still distorting the picture.
The consequences of this distortion are far-reaching and affect how we understand the world. When the contaminated data was fed into standard models used to measure risk, the models produced misleading results. For instance, a model analyzing the link between weather patterns and gas prices found a strong, statistically significant connection. However, when the researcher replaced the real weather data with a dummy variable that had no information at all—just a repeating calendar pattern—the model still found a strong connection. This proved that the model was not actually detecting a relationship between weather and price; it was simply reacting to the artificial calendar jumps in the data. The same false signal appeared in models designed to measure how much risk capital banks should hold. Using the uncorrected data, the models demanded nearly thirty percent more capital than necessary, a massive overestimation of risk that could lead to inefficient use of money.
Perhaps the most surprising finding was how this artifact changed the perceived relationship between different energy markets. Before the correction, the data suggested that the Asian gas market was isolated, moving independently from European gas and oil markets. Once the artificial jumps were removed, the data told a completely different story. The connection between these markets tripled, revealing that they were actually much more integrated than previously thought. The direction of influence even flipped, changing the view of Asian gas from a passive receiver of global shocks to an active transmitter of them. The study concludes that the free, continuous price series widely used by researchers is fundamentally flawed for serious analysis. It recommends that before any study begins, researchers must check the data for these calendar jumps and, if found, remove the specific days where the switches occur. Without this simple diagnostic step, conclusions about climate risk, market integration, and financial safety may be built on a foundation of false movement, mistaking the rhythm of a calendar for the pulse of the global economy.
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