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Market Impact Models: Participation Rates, Square Root Laws, and Depth

Article Quant Q&A · Author: shabbychef

Summary

The document surveys ways to estimate the price impact of large trades in liquid markets. A commonly used family relates impact per unit traded value to volatility and the trade’s share of volume during the execution interval, with an exponent between one-half and one. It also describes cost functions that scale with traded value to powers such as three-halves or five-thirds, which may require approximations for optimization.

Other approaches define realized impact from the prices of the first and last fills, while noting that this measure does not isolate the order’s effect from other trades or market events and is not itself a forecast. The square root law offers a simpler estimate based on volatility and the ratio of order size to daily volume, with a calibration constant. Order-book depth can also illustrate immediate execution costs. These are alternatives with different assumptions: the discussion does not provide a universal standard, calibration procedure, or evidence comparing predictive accuracy across models.

Key ideas

  • Impact estimates often relate volatility to a trade’s participation rate, raised to a model-dependent power.
  • Optimization models may use nonlinear cost functions that need approximation for some solvers.
  • First-fill to last-fill price change is a realized measure, not a clean forecast of an order’s causal impact.
  • The square root law estimates impact from volatility and order size relative to trading volume.
  • Order-book depth can approximate the cost of an immediate trade, but does not cover every impact mechanism.

Tags

Full text
# Is there a standard model for market impact?


# Is there a standard model for market impact?












Is there a standard model for market impact? I am interested in the case of high-volume equities sold in the US, during market hours, but would be interested in any pointers.

## Answer by gappy (score 28, accepted)

https://quant.stackexchange.com/a/277

There is a family of models that is so commonly used among practitioners that it can be almost regarded as standard. For a survey, check out Rob Almgren's entry in the Encyclopedia of Quantitative Finance. Check out also Barra, Axioma and Northfield's handbooks. In general, the impact term per unit traded currency is of the form

$$MI \propto \sigma_n \cdot \text{(participation rate)}^\beta$$

where the exponent is somewhere between 1/2 and 1, depending on the model being used, and the participation rate is the percentage of total volume of the trade, during the trading interval itself. When including the total MI in optimization, the models commonly used are the "3/2" model and the "5/3" model, in which the costs are proportional to (dollar value being traded for asset i)^{3/2, 5/3}. Since the term is not quadratic (and not solvable by a quadratic optimizer) some people approximate it by a linear term plus a quadratic one, or by a piece-wise linear convex function.

## Answer by Shane (score 10)

https://quant.stackexchange.com/a/46

I don't believe that there is a "standard" model (per se); in fact, there are many considerations around market impact models, so you would need to be more specific. At the most basic level, you might define market as $P_{first fill} - P_{last fill}$ once your order in actually in the order book (e.g. not including other costs like "opportunity cost"). This doesn't take into account any other trades that may be taking place at the same time or other events that might be impacting the price beyond your order. It doesn't help you to forecast market impact on an impending order (which would require some knowledge of time of day, volume, volatility, etc.).

That being said, I would certainly recommend reading "Optimal Trading Strategies" (Kissell, Glantz 2003) which gives a good overview (in addition to covering other transaction cost subjects).

## Answer by Mark Horvath (score 4)

https://quant.stackexchange.com/a/18929

In practice all impact models are sub-linear. Despite this is fact (seen in many academic publications, commercial and proprietary models), there is an interesting argument for using a linear impact models (other than being careful and pessimistic). Would anyone try to build a model, this approach would be also more parsimonious with less parameters to fit.

Imagine 10 VWAP orders of 10 different traders making up the buy trades of a day for a security, each of them trading the same amount. If impact would be sub-linear (concave) then only one of them trading $10 \times$ more, would result in a different impact. Assuming they are using the same VWAP algorithm (or same broker even), this would lead to contradiction as the impact should be the same.

You might also find this discussion useful: Quantopian Slippage Model.

## Answer by MaPy (score 3)

https://quant.stackexchange.com/a/40960

The square root law is a quite simple and popular model for price impact estimation:

$$\Delta p = Y\sigma\sqrt{\frac{Q}{V}}$$

where: $\Delta p$ is the price impact, $Y$ is a constant (needs to be calibrated). $\sigma$ daily volatility of the returns $Q$ daily trading volume.

There are a lot of papers around this model (e.g Gomes and Walbroeck 2015, Zarinelli et al. 2015)

## Answer by Thomas Baert (score 1)

https://quant.stackexchange.com/a/18456

The easiest way ,i suppose, would be to analyze the market depth. If there is a 20 cent gap between each 100 shares on the bid then to sell 1000 shares instantly would have an impact of $2. Your average price is the midpoint. There are more complicated formulations, but this seems to be how it works on simple examples such as bitcoin exchanges.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.