a narrow time range. the upper bound is perhaps around one day. the lower bound is a perpetual downward motion machine, as computing capacity compresses the timeline for trading. a decade ago, execution times on some electronic trading platforms dipped decisively below the one second barrier. as recently as a few years ago, trade execution times reached “ blink speed ” – as fast as the blink of an eye. at the time that seemed eye - watering, at around 300 – 400 milli - seconds or less than a third of a second. but more recently the speed limit has shifted from milli - seconds to micro - seconds – millionths of a second. several trading platforms now offer trade execution measured in micro - seconds ( table 1 ). as of today, the lower limit for trade execution appears to be around 10 micro - seconds. this means it would in principle be possible to execute around 40, 000 back - to - back trades in the blink of an eye. if supermarkets ran hft programmes, the average household could complete its shopping for a lifetime in under a second. imagine. it is clear from these trends that trading technologists are involved in an arms race. and it is far from over. the new trading frontier is nano - seconds – billionths of a second. and the twinkle in technologists ’ ( unblinking ) eye is pico - seconds – trillionths of a second. hft firms mackenzie ( 2011 ) provides a brilliant recent account of the role of hft, in particular in the context of the flash crash. hft is a sub - set of a broader class of algorithmic trading strategies. see also aldridge ( 2010 ). bis central bankers ’ speeches talk of a “ race to zero ”. this is the promised land of zero “ latency ” where trading converges on its natural ( planck ’ s ) limit, the speed of light. 4 the race to zero seems like a million miles from the european coffee shop conversations of the 17th century and the noisy trading floors of the 18th, 19th and 20th centuries. the dawn of electronic trading coincided with the dusk for floor trading. physical proximity lost its allure. as soon as computers processed faster than traders talked, the time was up for human interactions on physical exchanges. trading became virtual, distance a dinosaur. or so it seemed. latterly, however, hft is turning that logic on its head. the race to zero has encouraged traders to eke out the last pico -

tailedness and persistence in prices. when the holding period is short ( say t = 1 ), as with hft, the future price range is the volatility of the series magnified by the hurst coefficient. in other words, the fatter and more persistent the tails ( h ), the greater the market risk and the wider the bid - ask spread of the hft. this has implications for the dynamics of bid - ask spreads, and hence liquidity, among hft firms. during a market crash, the volatility of prices ( σ ) is likely to spike. from equation ( 1 ), fractality heightens the risk - sensitivity of hft bid - ask spreads to such a volatility event. 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 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 ). andrew lo ’ s “ adaptive market hypothesis ” is a more recent manifestation of essentially the same story. 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 ) ). 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. for a 14 - second period during the flash crash, algorithmic interactions caused 27, 000 contracts of the s & p 500 e - mini futures contracts to change hands. yet, in net terms, only 200 contracts were purchased. hft algorithms were automatically offloading contracts