Direct answer
Slippage can change during volatile markets because the execution path between your order intent and the final trade includes multiple time-sensitive and market-dependent steps. When volatility increases, the quoted or expected price can move quickly, liquidity can become thinner or temporarily unavailable, and the actual fill may occur after one or more delays. Together, these effects increase the chance that the fill price differs from what you anticipated.
Mechanics: what slippage is and what can change
Slippage is the difference between the price you expect at order submission and the price at which the order is actually filled. The “expected” price may be based on a displayed quote, a recent mid-price, or a model of where price should go. The filled price is determined by what the market can match at the moment the order (or its request) is processed.
A simple way to think about slippage is to separate stable mechanics from variable conditions:
- Stable mechanics (usually unchanged): an order travels from where you submit it to where it is matched, and the system must decide how to execute it given available liquidity and risk controls.
- Variable market conditions (change in volatility): the bid-ask spread can widen, price can jump between levels, and available liquidity at each price can drop.
- Variable execution conditions: network latency, system processing time, and order-handling logic (such as how partial fills and re-quoting are treated) can affect how quickly the system obtains a fill.
Why volatility tends to increase slippage
Volatility mainly changes slippage through four mechanisms:
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Price gap risk (the “now vs. then” problem): In volatile conditions, price can move significantly between the moment you base your expected price and the moment the order is executed.
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Latency (time delay): If there is delay between submission and processing, your order may see a different order book state than the one used to form the expected price. Even when the market is liquid, higher speed of movement can turn small timing differences into larger price gaps.
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Liquidity withdrawal and thinning: Markets may show fewer orders at certain price levels during sudden moves. If there is not enough liquidity where the system tries to fill, the execution may take the next available prices farther away.
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Order handling and partial execution: Some execution paths can result in partial fills, re-attempts, or different behavior under stress conditions. Those steps can produce a filled average price that differs from your initial expectation.
Worked example (with explicit assumptions)
Assume you submit a market order when the displayed price is 1.20000. Also assume:
- the system processes your order 200 ms after submission,
- during those 200 ms the market jumps upward,
- there is limited liquidity at 1.20000 but more liquidity at 1.20100.
If execution occurs only after the jump, the fill may happen around 1.20100. The slippage relative to your expected price is 1.20100 − 1.20000 = 0.00100 (in price terms). The key point is not the numbers, but that volatility plus time delay and liquidity availability can move execution to different price levels.
Material limitations and failure modes
Several limitations make slippage hard to predict and explain with one formula:
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“Expected price” is not always well-defined: Two people can form different expectations from the same market display (mid-price vs bid/ask vs last trade), which changes the slippage measurement.
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Observed slippage mixes multiple factors: Latency, spread changes, order book depth, and execution logic can overlap. A single observed increase in slippage does not prove which factor dominated.
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Market conditions can change mid-execution: Liquidity can appear and disappear quickly. A system may partially fill, then fail to complete under the available conditions, producing results that differ from a naive “instant fill” assumption.
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Time-sensitive behavior varies by execution venue and setup: Different execution models handle market moves, partial fills, and re-quoting differently. Without knowing the specific execution mechanism, you cannot generalize reliably.
How to verify it independently (without relying on predictions)
You can verify slippage mechanics by checking execution records against the exact price basis you consider “expected.” One practical approach is: