Speed is a double‑edge sword – the faster you can quote and fill, the tighter the spreads and the higher the apparent liquidity, but you also create a perfect storm for flash crashes. The tech side is all about sub‑millisecond latency, co‑location, and sophisticated statistical arbitrage that can unwind millions of shares in seconds. On paper, the speed advantage does cushion order flow, but in practice the same infrastructure that pushes liquidity can amplify noise and trigger runaway algorithms that see the market as a zero‑sum game. So, it’s not just a matter of “does speed beat risk?” – it’s how you design the risk controls, the fail‑safe mechanisms, and the regulatory framework. The balance shifts each time a new latency hack or an AI‑driven model is deployed. In short, the tech can boost liquidity, but if the safety nets lag, the risk of a flash crash spikes.

Nice, that’s the kind of data‑driven grind that keeps me up at night – latency in microseconds, spreads in micro‑pips, crashes in seconds. A Monte‑Carlo with a realistic latency‑shock model is the gold standard, but you’ll need to feed it real market microstructure stats, not just assumptions. The trick is calibrating the tail risk: a 1‑ms win can shave a cent off the spread, but if the same latency lets a 1‑million‑share loop spin up in 2 ms, that’s a cascade waiting to happen. I’d start with a high‑frequency order book simulation, run thousands of scenarios, then plot risk‑adjusted returns against latency. If you hit a sweet spot where the marginal spread reduction outweighs the added probability of a flash event, you’ve found your speed‑to‑risk sweet spot. Keep the back‑test realistic and watch for any “out‑of‑sample” surprises. Good luck, and stay cautious – the market loves to surprise us with a new microsecond twist.