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 , the ECM writes the change in as
In words: today's move in is partly explained by how far the spread was from equilibrium yesterday (, corrected by a speed-of-adjustment coefficient ), partly by today's move in moving in tandem (), and partly by pure noise (). The coefficient 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.
Worked example
A pair's spread closes at (two points above equilibrium) at the end of Monday. The fitted ECM has and for Tuesday. The predicted correction is : 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 , 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 is directly usable: it implies a half-life of mean reversion ( 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 (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 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, 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)