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July 8, 2026
Agricultural producers, grain merchandisers, food companies, and policymakers all face the same challenge: commodity prices can be extremely volatile. Prices for corn, soybeans, cattle, coffee, among others, often appear to move randomly due to unpredictable events such as weather, geopolitical conflicts, economic conditions, and changing consumer demand.
But what if part of that volatility is not truly random?
This is essentially the question behind our recent study. But before we get there, let’s briefly review the mainstream approach to price volatility.
The traditional view in economics: markets react to outside events
Economic models traditionally rely on the idea of equilibrium (i.e., a stable balance between supply and demand), and prices fluctuate because markets are affected by external events (such as weather, government policies, technological innovations, exchange rate movements, disruptions in the energy market, and consumer preferences). When these events occur, prices adjust and eventually move toward a new equilibrium. In this view, price volatility is seen as a natural market response to outside changes that affect supply and demand, and markets have a natural tendency to self-correct. This framework has guided agricultural economics research for decades and forms the basis of many forecasting and risk management tools used today.
However, real-world data often tell a different story. Commodity prices exhibit persistent volatility, irregular cycles, and sudden large swings that are difficult to explain using traditional models. Even more puzzling, these patterns do not always disappear over time.
This has led researchers to ask a further question: What if this instability is not coming from outside the market, but at least some of it comes from within the market?
An alternative view: markets can create their own volatility
Our study explores a different possibility. Instead of viewing price volatility solely as the result of outside forces, we investigate whether commodity markets may generate some of their own fluctuations through complex interactions among producers, consumers, traders, storage decisions, and expectations.
In other words, market participants reacting to one another may create patterns of price movement that appear random but actually emerge from the internal structure of the market itself. This phenomenon is known as nonlinear deterministic dynamics. It is nonlinear because cause and effect are not proportional, i.e., a small change can sometimes have a huge effect, while a large change may have only a small effect. It is deterministic because there are specific patterns in the system (market), even if they are not clearly identifiable. And it is dynamic because the behavior (say, price behavior) evolves over time through feedback loops, meaning that a certain outcome today will have an influence on future outcomes (e.g., today’s price will lead to certain decisions from producers and other market participants, and these decisions will have an influence on prices in the future).
This idea is sometimes associated with what is popularly known as “chaos theory.” Note that, in this context, chaos does not mean complete disorder. It describes instead systems that follow specific patterns yet produce outcomes that appear random. These systems are also very sensitive to small changes, which brings a famous example from chaos theory in which a butterfly flaps its wings in Nepal and contributes to a tornado in Nebraska. This is known as the “butterfly effect,” and it obviously doesn’t mean that butterflies create tornadoes. The idea here is that, in some systems (markets), even a tiny difference or change in current conditions can make the system follow one pattern instead of another and lead to dramatically different results in the future.
More broadly, the butterfly effect is about knowledge. Traditional thinking assumes that we can predict future events accurately as long as we collect enough information. Chaos theory says instead that, even if we have enough information and understand the system perfectly, our measurements are never perfect, and tiny errors can grow exponentially and lead to large differences in outcomes in the future.
A useful analogy is weather forecasting. Jokes apart, meteorologists can predict tomorrow reasonably well (not perfectly, but reasonably well). However, they struggle much more with forecasts a month from now. But this doesn’t mean there is a problem with physical laws. The atmosphere follows physical laws perfectly. The issue is that tiny differences or changes in current conditions can grow over time and lead to very different outcomes weeks later.
Back to our research: What exactly did we do?
We examined daily futures prices covering 50-60 years for ten agricultural commodities: corn, soybeans, wheat, cotton, coffee, sugar, live cattle, feeder cattle, hogs, and orange juice. Rather than relying on statistical approaches traditionally used in economics, we used techniques from nonlinear time-series analysis (such as phase space reconstruction, nonlinear predictive methods, and permutation entropy). Essentially, these methods are designed to detect whether apparently random fluctuations may contain hidden structures.
What did we find?
The results were quite interesting. The analysis found evidence that a very large portion of the variation in all commodity prices was contained in structured signals rather than random noise. Technically speaking, these signals were consistent with low-dimensional nonlinear deterministic dynamics.
Now, in plain language, this means that a substantial portion of commodity price volatility may reflect recurring internal market dynamics interacting in complex ways, rather than being entirely driven by random external events. Although we found similar evidence across all ten commodity markets, the strength and complexity of the patterns varied from one commodity to another.
What does this mean for farmers and agribusinesses?
The findings do not mean commodity prices will suddenly become easy to predict.
In fact, the study emphasizes that long-term forecasting remains extremely difficult because these systems are highly sensitive to initial conditions. Small changes or measurement errors today can lead to large forecasting errors months or years into the future.
However, the results suggest that short-term forecasting may be improved by using methods capable of capturing nonlinear patterns. This may help explain why some recent studies show that machine learning and artificial intelligence techniques can outperform traditional statistical models.
For producers and agribusiness managers, this could eventually lead to better short-term price forecasts, improved marketing decisions, more accurate hedge ratio estimation, enhanced risk management strategies, and better timing of purchases and sales.
Implications for agricultural policy
The study may also have implications for government policy. If price volatility is caused mainly by external events, policymakers might conclude that markets naturally self-correct and that intervention may be unnecessary. However, if a portion of price volatility arises from internal market dynamics, markets may not always move automatically toward stability. In this case, carefully designed stabilization policies could potentially improve economic outcomes.
On the other hand, nonlinear systems can also respond unpredictably to policy changes. A small intervention may produce large effects, while a large intervention may have little impact. This means policymakers should exercise caution when designing programs intended to reduce commodity price volatility.
Going forward
Our study does not imply that commodity prices are purely deterministic or that traditional economic models are useless. External shocks clearly matter as we witness regularly how weather, trade policy, geopolitical events, and macroeconomic conditions influence agricultural markets.
Instead, our research suggests that commodity prices may be driven by a combination of external events and internal market dynamics. Understanding both forces could lead to better forecasting models, improved risk management tools, and more effective policy design.
For farmers, traders, and agricultural economists, the key message is simple: commodity markets may be more complex—and potentially more structured—than they first appeared. What often looks like randomness may actually contain hidden patterns that, if understood correctly, can provide valuable insights into how agricultural markets evolve over time. And this is the next step for research in this field. Stay tuned!
Fabio Mattos
Associate Professor
Department of Agricultural Economics
University of Nebraska-Lincoln
fmattos@unl.edu
Sagar Dahal
Ph.D. Student
Department of Economics
Iowa State University
sdahal3@iastate.edu