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Error Correction Models

An error correction model says two cointegrated series can wander in the short run, but every deviation from their long-run relationship gets partly corrected the next period.

Prerequisites: Cointegration, Spread Construction and Hedge Ratios

Two cointegrated stock prices can each wander like random walks on their own, while the gap between them keeps snapping back toward a stable long-run level. Knowing they're cointegrated tells you the gap is mean-reverting; it doesn't tell you how fast, or what to do with a day-to-day change in either price. An error correction model (ECM) answers both.

An ECM splits each period's price change into two pieces: the usual short-run noise, plus a pull back toward equilibrium proportional to how far last period's price was from that equilibrium. The size of that pull is a single number you can read off the model directly.

The two pieces of a price move

For a cointegrated pair with spread zt1=yt1βxt1z_{t-1} = y_{t-1} - \beta x_{t-1}, the ECM writes the change in yy as

Δyt=αzt1+γΔxt+εt\Delta y_t = \alpha \, z_{t-1} + \gamma \, \Delta x_t + \varepsilon_t

In words: today's move in yy is partly explained by how far the spread was from equilibrium yesterday (zt1z_{t-1}, corrected by a speed-of-adjustment coefficient α\alpha), partly by today's move in xx moving in tandem (γΔxt\gamma \, \Delta x_t), and partly by pure noise (εt\varepsilon_t). The coefficient α\alpha is the whole point: it must be negative for the relationship to be mean-reverting, and its size tells you what fraction of yesterday's dislocation gets closed out today.

equilibrium, z = 0 α: fraction corrected
The spread drifts away from zero, then each subsequent step closes part of the gap — that closing fraction is α, the error correction coefficient.

Worked example

A pair's spread closes at zt1=2.00z_{t-1} = 2.00 (two points above equilibrium) at the end of Monday. The fitted ECM has α=0.25\alpha = -0.25 and γΔxt0\gamma \, \Delta x_t \approx 0 for Tuesday. The predicted correction is Δyt=0.25×2.00=0.50\Delta y_t = -0.25 \times 2.00 = -0.50: yy is expected to fall by 0.50 on Tuesday, closing a quarter of the two-point gap, leaving roughly 1.50 still open. If instead α=0.60\alpha = -0.60, the same two-point dislocation predicts a 1.20 correction — a much faster-reverting pair, and a much shorter expected holding period for a spread trade entered at that level.

What this means in practice

The size of α\alpha is directly usable: it implies a half-life of mean reversion (ln(0.5)/ln(1+α)\ln(0.5)/\ln(1+\alpha) periods, roughly), which tells a stat-arb desk how long to expect a position to take to pay off and therefore how much capital it ties up. A very small α|\alpha| (close to zero) means the spread is technically cointegrated but corrects so slowly that transaction costs and financing can eat the edge before it mean-reverts.

A statistically significant α\alpha from a historical fit describes how the pair behaved over the sample, not a law it must obey going forward. If the underlying businesses diverge, α\alpha can drift toward zero or flip sign, and a spread that used to correct will simply stop — see structural break tests for how to catch that early.

Related concepts

Practice in interviews

Further reading

  • Engle & Granger, 'Co-integration and Error Correction: Representation, Estimation, and Testing', Econometrica (1987)
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