Market Impact Models

Understanding market impact

Market impact represents the effect that a trade has on the price of an asset. When executing large orders, traders must balance two competing factors:

1. Execution speed - faster execution reduces timing risk but increases market impact
2. Price deterioration - slower execution may lead to higher overall costs due to price drift

Market liquidity directly affects impact, with more liquid markets generally exhibiting lower impact costs.

Components of market impact models

Temporary impact

Temporary impact represents short-term price movements that occur during order execution but typically reverse shortly afterward. This is often modeled as a concave function of order size:

Permanent impact

Permanent impact reflects lasting price changes that remain after the order completes. This component is particularly important for algorithmic execution strategies.

Mathematical frameworks

Square root model

One common approach models impact as proportional to the square root of order size relative to average daily volume:

Impact = σ * √(V/ADV)

Where:

• σ = Asset volatility
• V = Order volume
• ADV = Average daily volume

Linear impact model

Used primarily in high-frequency trading, linear models assume impact increases proportionally with order size:

Impact = k * (V/ADV)

Where k is an asset-specific coefficient.

Applications in trading

Optimal execution

Market impact models are essential for:

• Smart Order Router (SOR) logic
• Trade execution quality measurement
• Cost prediction and optimization

Risk management

Impact models help assess:

• Portfolio liquidation costs
• Maximum position sizes
• Real-time risk assessment

Market impact measurement

Data requirements

Accurate impact modeling requires:

• Tick-by-tick price data
• Order book depth
• Volume profile analysis
• Transaction cost modeling capabilities

Calibration techniques

Models must be regularly calibrated using:

• Historical trade data
• Market microstructure analysis
• Realized execution costs

Advanced considerations

Cross-asset effects

Modern impact models account for:

• Cross-asset correlation
• Market regime changes
• Market microstructure noise

Machine learning approaches

Recent developments include:

• Neural network-based impact prediction
• Adaptive calibration methods
• Real-time model adjustment

Regulatory implications

Impact models play a crucial role in:

• Best Execution Policies (MiFID II & SEC)
• Market surveillance systems
• Trade reconstruction requirements