Direct answer
During volatile markets, liquidity often becomes thinner and less predictable, so the cost to execute trades rises. That cost is reflected in wider bid–ask spreads and in a higher chance that orders are filled at worse prices than expected.
Mechanics: definitions and the basic chain of effects
Liquidity is how easily market participants can buy or sell around a price without moving it much. In practice, it depends on how many orders rest near the current price and how quickly they can be matched.
Spread is the difference between the best bid (buy) and the best ask (sell). A wider spread means the market is paying a larger “gap” between buyers and sellers, typically because matching is harder.
A simple cause-and-effect chain is:
- Volatility increases: prices move quickly and the next price may be harder to estimate.
- Order availability changes: fewer participants may be willing to quote prices, or existing quotes may be pulled.
- Matching becomes less efficient: the market has less depth near the current price.
- Spreads widen: market-makers or liquidity providers demand compensation for higher uncertainty and slower matching.
Evidence or example: gaps, latency, and liquidity withdrawal
Consider an example with clear assumptions, without relying on live prices:
- Assume the market shows a best bid of 1.1000 and best ask of 1.1002, so the spread is 2 “points” (in decimal price terms).
- Then price starts moving rapidly upward because new information arrives and many orders are repriced.
Two related effects can appear:
1) Liquidity withdrawal reduces near-price depth If orders are canceled faster than new orders are posted, the best bid/ask can jump outward. Even if trading continues, there may be fewer resting orders close to the last traded price. This can widen the spread.
2) Gaps and execution vs. quoted price If an order arrives while quotes are changing, the order may not fill at the quote you first saw. Instead, it may fill at the next available price level, which can be farther away during volatility. That is one way “gaps” can show up between a displayed quote moment and the eventual fill.
Latency matters even without assuming any specific technology:
- If it takes time for an order to reach the matching venue (network + system processing), a fast-moving market can change the best bid/ask between “quote time” and “execution time.”
- In that case, the spread itself may not be the only issue; price movement during the delay can widen the effective trading cost.
Limitations and risks: what can fail, and why verification matters
Material limitations to keep in mind:
1) Liquidity is not constant Liquidity can change within seconds. What is “tight” under normal conditions can become “thin” during bursts of volatility, so historical relationships may not hold.
2) Order type and market microstructure affect outcomes Different order types and handling rules can change how your order interacts with changing liquidity. During volatility, queues, partial fills, or trading through multiple price levels can increase uncertainty.
3) Calculation assumptions must match reality If you estimate cost using only the displayed spread, you may miss slippage from fast price movement, gaps, and partial matching. Any example calculation should state assumptions about timing (latency), available depth, and whether the quote remains valid until execution.
One common failure mode is assuming that “the current spread” will apply to the eventual fill. In volatile markets, that assumption can break.
Verification and next questions
To independently verify these ideas, you can compare how bid–ask spreads and trade prices behave during rapid moves in a controlled dataset or historical chart, while noting:
- whether the spread widens at the same time as price jumps,
- whether fills occur beyond the displayed best bid/ask,
- whether periods of thin liquidity coincide with sudden spread changes.
A helpful next question is: How do different spread definitions and calculation methods (for example, point-based versus time-sampled views) affect what you observe as “the spread”? You can also examine common execution gaps by contrasting displayed quotes with the prices shown for executed trades.