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Closing Auction Impact Regimes: Zero-Then-Linear Practical Playbook

2026-03-21 · finance

Closing Auction Impact Regimes: Zero-Then-Linear Practical Playbook

Closing auctions are one of the few moments where very large size can clear at a single price.

But that does not mean impact is always flat.

Recent microstructure evidence suggests a useful execution mental model:

  1. a zero-impact buffer for small added auction flow,
  2. a roughly linear impact zone for medium flow,
  3. a nonlinear/superlinear zone for very large flow.

This playbook turns that into practical sizing and routing rules.

One-Line Intuition

At the close, impact is piecewise: treat your order as a regime-switch problem, not a single curve fit.

1) What the data says (high-level)

A) Price impact at auction is often piecewise, not smooth

Empirical auction studies on Euronext show impact that is:

That is the core “zero, then linear” result.

B) Auction dynamics accelerate into the close

As uncross approaches:

So the same notional size can have very different impact at 15:56 vs 15:59.

C) Closing-auction effects are not purely intraminute noise

Cross-venue evidence (Nasdaq vs NYSE) reports:

D) Heavy tails remain real in close returns

Even with auction concentration, close returns can show heavy tails. This is why tail controls (not only mean IS) are mandatory.

2) A practical piecewise slippage model

For incremental close slice size (q) (as %CADV or %auction volume), model expected impact in spread units:

[ \Delta p(q) = \begin{cases} 0 & 0 \le q \le q_0 \ \beta_1 (q-q_0) & q_0 < q \le q_1 \ \beta_1 (q_1-q_0) + \beta_2 (q-q_1)^\gamma & q > q_1 \end{cases} ]

with:

Execution cost for a close parent then becomes:

[ \mathbb{E}[IS_{close}] \approx \Delta p(q) + C_{timing} + C_{residual} + C_{fees} ]

where residual captures fallback if auction fill is insufficient.

3) Feature set that actually helps in production

Estimate (q_0, q_1, \beta_1, \gamma) conditionally on state:

Key principle: regime boundaries are state-dependent.

4) Sizing policy from the model

Define three zones at decision time:

A simple policy objective for close program:

[ \min_{q_t} \sum_t \mathbb{E}[IS_t(q_t)] + \lambda \cdot CVaR_{95}(IS) ]

subject to completion and risk constraints.

If Red-zone probability rises, shift volume earlier or diversify venues.

5) Calibration protocol

  1. Normalize size by both CADV and auction paired volume.
  2. Fit piecewise model by symbol-liquidity bucket and venue.
  3. Refit weekly/monthly; monitor drift in (q_0, q_1).
  4. Track calibration by clock buckets (15:55–15:56 ... 15:59–16:00).
  5. Backtest with realistic cutoff/eligibility constraints.

Do not judge success with only average IS. Track:

6) Failure modes

7) Minimal implementation checklist

Bottom line

Closing auction execution is not “always cheap liquidity.”

It is state-dependent liquidity with regime boundaries.

Model those boundaries explicitly (zero -> linear -> convex), and your close router can take size when buffer exists while avoiding the superlinear tax when crowding appears.

References