I think the point they're going after is that algorithmic trading behavior can be meaningfully sensitive to rounding errors (which seems plausible if you profit by amplifying tiny signals), so in the context of a simulation you might still have components like Black Scholes, but for the trades themselves (even simulated) you need to take more care or risk an excessive error.

In other words, they're describing a scenario where 1 in 10^14 error is potentially not tolerable because of some amplified discrete behavior.