In other words, liquidity under stress is likely to prove less resilient. This is because one extreme event, one flood or drought on the Nile, is more likely to be followed by a second, a  18  This finding can be given a variety of behavioural interpretations, including persistence in gaps in the limit order book (Bouchard et al (2009)). Empirical support for this hypothesis is found in Easley et al (2011a). 19  Andrew Lo’s “adaptive market hypothesis” is a more recent manifestation of essentially the same story. 20  Equation (1) holds asymptotically in expectation for large T and for a broad range of statistical processes (see Peters (1994) and Qian and Rasheed (2004)). 8  BIS central bankers’ speeches  third and a fourth. Reorganising that greater risk, market makers’ insurance premium will rise accordingly. This is the HFT inventory problem. But the information problem for HFT market-makers in situations of stress is in many ways even more acute. Price dynamics are the fruits of trader interaction or, more accurately, algorithmic interaction. These interactions will be close to impossible for an individual trader to observe or understand. This algorithmic risk is not new. In 2003, a US trading firm became insolvent in 16 seconds when an employee inadvertently turned an algorithm on. It took the company 47 minutes to realise it had gone bust. 21 Since then, things have stepped up several gears.
Finally, I wish you a productive and fruitful time here at the HKMA Fintech Day, and trust that you will benefit from today’s rich plenary and parallel sessions. 21. Thank you very much. 4/4  BIS central bankers' speeches