Disequilibrium vs Equilibrium Macro Models in Volatile Times
June 26, 2025
As global markets undergo one of their most uncertain periods in decades, economists and investment professionals alike are reaching for economic models to plot a way forwards.
But are current models up to the job of making sense of complex systems? Ongoing market turmoil is a reminder of how economic change can be unpredictable and disruptive. This is true whether the catalyst be abrupt policy shifts, rapid technological change, or shifts in the global energy system.
Traditional models used by investment professionals today, known as ‘linear’ or ‘equilibrium’ models, provide an incomplete understanding of these kinds of systems change. As a result, assets are mispriced, financial hazards go unexamined, and lucrative opportunities are missed.
Trex is different. We use a ‘disequilibrium’ framework to produce data and insights on systems change for financial professionals, empowering them to make well-informed decisions and construct portfolios that are resilient to disruptions.
Advantages of Non-linear ‘Disequilibrium’ Models
A key advantage of the disequilibrium approach is the ability to capture volatility, irrational behavior, and non-linear dynamics — features essential for modelling disruptive systems change.
One example of a disequilibrium model is E3ME, created by Cambridge Econometrics to assess global policy challenges. This is able to capture the distinctly non-linear nature of the energy transition as part of a coherent picture of the macroeconomy. When combined with realistic, credible scenarios, the output of this model should prove more useful to investment professionals working to simulate a disorderly world.
Equilibrium Models: Perfect Markets in an Imperfect World
Equilibrium models dominate macroeconomic forecasting. You may know them by their acronyms: Computable General Equilibrium (CGE) and Dynamic Stochastic General Equilibrium (DSGE) are two popular iterations.
These models assume that markets always balance — that supply matches demand and all resources are optimally allocated. Businesses and households are portrayed as perfectly rational actors, operating with complete knowledge of present and future conditions.
While elegant, these assumptions are painfully out of step with the chaotic realities of financial markets and climate risks. Here’s why:
1. Static assumptions in a dynamic world: Equilibrium models do not reflect the volatility of the real world. Typically, they assume economic actors optimise their costs and are armed with perfect information, if not foresight. These assumptions crumble during periods of rapid transition or transformative change.
2. Limited scope for innovation and instability: These models are less well equipped to account for disruptions like financial crises or the rapid adoption of breakthrough technologies. By design, they assume resources are fixed and fully utilised and arguably do not adequately capture the effects of innovation and policy shifts.
3. Over-Reliance on Price Signals: Equilibrium models focus heavily on price changes as the sole driver of behaviour. They downplay other critical factors, such as regulatory interventions, investment incentives, labour market participation, and structural transformations — all of which form part of a realistic policy package and set of outcomes.
These limitations make equilibrium models poorly suited for financial professionals navigating systems change, like the low-carbon transition, where uncertainty, innovation, and structural change are the rule, not the exception.
Enter Disequilibrium Models: A Dynamic Alternative
Disequilibrium models embrace the inherent messiness of real-world systems. By shedding the assumption of perpetual market balance, they can simulate instability, innovation, and structural change. This makes them far better equipped to capture the complexities of climate risks and opportunities. Disequilibrium models are already used to forecast the weather — so why shouldn’t they be used to project how climate change and the energy transition will transform the economy?
Here’s how disequilibrium models shine:
1. Mimicking real-world complexity: Disequilibrium models, like Cambridge Econometrics’ E3ME model, attempt to mimic how the global economy actually works based on detailed theories and empirically observed relationships. When fed appropriate scenario assumptions, they are capable of producing credible, realistic outputs that reflect potential government policy decisions, geopolitical events, labour market disruptions, and shifts in interest rates.
2. Modelling systemic shocks: These models can simulate how financial markets react to major upheavals, such as an abrupt policy shock or a rapid decline in fossil fuel demand. They allow for scenarios where markets don’t just “adjust” but fundamentally transform.
3. Policy-driven innovation: Unlike equilibrium models, which often treat technological progress as an external input, disequilibrium models integrate it as a dynamic outcome. This means they are able to simulate how policies — like clean energy mandates or carbon taxes — drive innovation, cost reductions, and technology adoption.
4. Feedback loops: These models are built to capture feedback effects in complex systems. For example, they can simulate how a shift to renewable energy affects job markets, trade balances, and GDP growth, creating ripple effects across the economy.
Although no single model has fully integrated all the capabilities referenced above, disequilibrium approaches collectively have the ability to provide the detailed, real-world insights demanded by investors given today’s volatile geopolitical environment.
A Case in Point: Disequilibrium vs Equilibrium
Say a financial professional wants to estimate the economic impacts of the low-carbon transition to inform their portfolio strategy. An equilibrium model would likely predict a net economic cost, assuming that low-carbon investments would displace more efficient uses of capital. In contrast, the E3ME disequilibrium model could project either a net cost or a net benefit, depending on the characteristics of the country and the sectors that power its economic growth.
Furthermore, the E3ME output would account for rapid structural transformation, recognising that equilibrium cannot accurately reflect an economy in flux.
Which prediction seems more aligned with real-world dynamics? Which one would you want to inform your portfolio strategy?
Why This Matters
Financial professionals need credible, reliable data to do their jobs. Equilibrium models may be helpful in the classroom, but are less so when it comes to pricing risks and opportunities amidst the greatest system change of our lifetimes — the global energy transition.
By integrating disequilibrium models into their toolkit, financial professionals can:
● Better understand how policies and innovations reshape markets.
● Anticipate systemic risks, from stranded assets to green bubbles.
● Identify opportunities in emerging sectors and technologies.
Put simply, disequilibrium models produce the nuanced insights needed to ride the rollercoaster of systems change.