The Fear Index

volatility, in our opinion, is a function of digitalisation, which is exaggerating human mood swings by the unprecedented dissemination of information via the internet.’
    ‘And we’ve found a way to make money out of it,’ said Quarry happily. He nodded at Hoffmann to continue.
    ‘As most of you will be aware, the Chicago Board of Exchange operates what is known as the S and P 500 Volatility Index, or VIX. This has been running, in one form or another, for seventeen years. It’s a ticker, for want of a better word, tracking the price of options – calls and puts – on stocks traded in the S and P 500. If you want the math, it’s calculated as the square root of the par variance swap rate for a thirty-day term, quoted as an annualised variance. If you don’t want the math, let’s just say that what it does is show the implied volatility of the market for the coming month. It goes up and down minute by minute. The higher the index, the greater the uncertainty in the market, so traders call it “the fear index”. And it’s liquid itself, of course – there are VIX options and futures available to trade, and we trade them.
    ‘So the VIX was our starting point. It’s given us a whole bunch of useful data going back to 1993, which we can pair with the new behavioural indices we’ve compiled, as well as bringing in our existing methodology. In the early days it also gave us the name for our prototype algorithm, VIXAL-1, which has stuck all the way through, even though we’ve moved way beyond the VIX itself. We’re now on to the fourth iteration, which with notable lack of imagination we call VIXAL-4.’
    Klein jumped in again. ‘The volatility implied by the VIX can be to the up side as well as the down side.’
    ‘We take account of that,’ said Hoffmann. ‘In our metrics, optimism can be measured as anything from an absence of fear to a reaction against fear. Bear in mind that fear doesn’t just mean a broad market panic and a flight to safety. There is also what we call a “clinging” effect, when a stock is held in defiance of reason, and an “adrenalin” effect, when a stock rises strongly in value. We’re still researching all these various categories to determine market impact and refine our model.’ Easterbrook raised his hand. ‘Yes, Bill?’
    ‘Is this algorithm already operational?’
    ‘Why don’t I let Hugo answer that, as it’s practical rather than theoretical?’
    Quarry said, ‘Incubation started back-testing VIXAL-1 almost two years ago, although naturally that was just a simulation, without any actual exposure to the market. We went live with VIXAL-2 in May 2009, with play money of one hundred million dollars. When we overcame the early teething problems we moved on to VIXAL-3 in November and gave it access to one billion. That was so successful we decided to allow VIXAL-4 to take control of the entire fund one week ago.’
    ‘With what results?’
    ‘We’ll show you all the detailed figures at the end. Off the top of my head, VIXAL-2 made twelve million dollars in its six-month trading period. VIXAL-3 made one hundred and eighteen million. As of last night, VIXAL-4 was up about seventy-nine-point-seven million.’
    Easterbrook frowned. ‘I thought you said it had only been running a week?’
    ‘I did.’
    ‘But that means …’
    ‘That means,’ said Ezra Klein, doing the calculation in his head and almost jumping out of his chair, ‘that on a ten-billion-dollar fund, you’re looking at making a profit of four-point-one-four billion a year.’
    ‘And VIXAL-4 is an autonomous machine-learning algorithm,’ said Hoffmann. ‘As it collects and analyses more data, it’s only likely to become more effective.’
    Whistles and murmurs ran around the table. The two Chinese started whispering to one another.
    ‘You can see why we’ve decided we want to bring in more investment,’ said Quarry with a smirk. ‘We need to exploit the hell out of this thing before anyone develops a clone strategy. And now, ladies and gentlemen, it seems to me that this might be a suitable moment to offer you a glimpse of VIXAL in operation.’

    THREE KILOMETRES AWAY, in Cologny, forensics had completed their examination of the Hoffmanns’ house. The scene-of-crimes officers – a young man and woman, who might have been students or lovers – had packed up their equipment and left. A bored gendarme sat in his car on the drive.
    Gabrielle was in her studio, dismantling the