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In the book by Lehalle and laruelle - "market microstructure in practice" -

"The trading activity of HFT updates limit orderbooks at a higher rate than the round trip for any non-colocated observer. The snapshot of the limit orderbooks you take into account to make decisions does not, in most cases, reflect the state of the book when your order actually reaches it... when the orderbook update frequency f is larger than two times the traveling time information (i.e. f > 1/(2τ)), the decision-making process does not use the proper information to decide where and how to route an order. "

However, we don't see this being used in any research paper as a threshold for market efficiency estimates. From the point of view of a market practitioner, there should be lower bound at which exchanges have to segment their market data feeds to a higher number - such that these feeds are usable by the participants.

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This is not exactly answer but comment's length was not enough.

When you do collocation, there are certain conditions in SLA that you could use as benchmark (e.g. max latency of 1millisec for data feed, 10millisec for order etc.), but if your model takes this into account it would be better to estimate it by yourself and get average instead of worst case scenario.

Exchanges/regulators regularly publish papers arguing why their rules/decisions improves market efficiency/fairness, but you can find papers that argue exactly the opposite. That makes it hard for the researcher to decide which side to pick.

In most situations/models latency increases the uncertainty (of whatever you are trying to do), and that is taken into account by some other factor. Almost every backtest engine I have worked with, has an option for fixed or bounded random latency and usually results are the same when you add/subtract 1 millisecond (apparently in real life orderbooks are not as dynamic in 1 millisecond most of the time).

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