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Valuing Limit Order Queue Position Under Cancellation Risk

Article Quant Q&A · Author: Richard
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Summary

The document asks how a market maker might assign monetary value to an order resting at a particular price level and queue position, using BTC-USD as an example. It offers no valuation formula or empirical method. The response says that pricing queue position this way is difficult and that the author knows of no literature addressing it directly.

A key complication is the option to cancel a quote quickly when news changes its value. A useful assessment would therefore need to account for how fast a participant can receive and act on different information, such as prices on another exchange or a social media feed. Those sources can have different latencies. The discussion is conceptual rather than a tested framework; it does not specify how to model queue fills, cancellation timing, or the monetary value of those effects.

Key ideas

  • Queue position may have economic value to a market maker, but the document gives no formula for measuring it.
  • The ability to cancel a quote in response to news can affect the value of a resting order.
  • Information sources may arrive at different speeds, and cancellation latency can vary across participants.
  • The response identifies a modeling challenge rather than presenting empirical evidence or a complete method.

Tags

Market makingHigh-frequency tradingMarket microstructureExecution
Full text
# queue position value in all limits in the book


# queue position value in all limits in the book












How would you evaluate the value of an order in a given limit at a given queue position in the order book ? For example let's say I am a market maker in BTC-USD and I would like to play some HFT games and value my orders at level greater than the first one. How would you do this or are you aware on literature dealing with this ?

## Answer by Bob Jansen (score 1)

https://quant.stackexchange.com/a/60382

Assigning a monetary value to queue position in this way seems very hard. I myself am not aware of any literature on this. What makes this problem particularly hard is that there is also value in being able to quickly cancel the quote in response to news which I think should be incorporated somehow. How quick one can cancel would depend on the participant and per participant on the source of the news. For example, the source could be prices on another exchange or a Twitter feed. This data is retrieved with different levels of latency.

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