The VIX, or Volatility Index, is Wall Street’s fear gauge. I first started paying attention to it in the late 1990s. Back then, a level of around 20 seemed normal. If the index got to 30, that was an indication of serious market unease; over 40 signalled a crisis. The highest the VIX ever got was during the 1987 stockmarket crash, when it reached 150. In the 2008 global banking crisis, it peaked at just below 90.
The US economy has gradually recovered from the banking crisis, and the newly legislated tax cuts will further boost corporate profitability. These effects, though, are now ‘priced in’: share prices have already risen to reflect them. Tax cuts aside, the political system remains largely paralysed. The Federal Reserve seems likely to continue raising interest rates, which usually isn’t good news for the price of shares, and is beginning the process of weaning markets off the flood of cheap money that has helped inflate share prices. The tax cuts will most likely increase the Federal deficit. Add in a president who is the very opposite of calm (and who is under FBI investigation), and you might expect the VIX to be approaching the sweaty-palmed 30s. It isn’t. As this issue of the LRB went to press, the VIX was 9.8. It has been low for many months, and shows no clear sign of increasing.
Donald Trump would no doubt attribute the low readings to investors’ confidence in his leadership. But I have my doubts. There is an alternative explanation. Heisenberg’s uncertainty principle is often taken to mean that whenever you measure something, you alter it. In the everyday world, you can usually set this aside: I don’t worry about the effect of the speedometer on how fast my car’s wheels turn or on how its engine runs. You can’t ignore it, though, in economic life. As Charles Goodhart argues, if a measurement device is widely used, it stops being a simple economic speedometer. In the financial markets, it becomes part of how traders think, and can then begin to affect how they act.
The VIX came about through two iterations of this process. The first began in 1973, when the world’s first organised options exchange was set up in Chicago. An option gives its holder a right, but not an obligation. A ‘put option’, for example, is the right to sell an asset such as a block of shares at a pre-set price on, or at any time before, the date on which the option expires. A ‘put’ can therefore function as a kind of insurance, limiting the losses that the owner of the asset can suffer. A right of that kind is clearly valuable, but it’s far from obvious how to measure its value. Making simplifying assumptions of the kind common in economics, Fischer Black, Myron Scholes and Robert C. Merton devised an elegant way of doing just that. Their mathematical model of options prices was quickly picked up by options traders, who started to use it in Chicago’s crowded trading pits. In so doing, they changed patterns of options prices (which originally corresponded only roughly to the postulates of Black and his colleagues) so that they fitted the model much more closely. As Timothy Mitchell of New York University has put it, ‘the effectiveness of economics rests on what it does, not on what it says.’[*]
The second iteration also involved economists, initially at least. The crucial parameter in the Black-Scholes-Merton model is the volatility of the shares under option: the degree of fluctuation in their price. One of the simplifying assumptions they made was that the volatility of any given stock was constant, but traders couldn’t bring themselves to believe that. Nor did Black, Scholes, Merton or any other economist take that assumption literally. But both practitioners and economists realised that you could use the options model backwards, so to speak: you could start with the market price of an option, and calculate the level of volatility of the underlying shares that was consistent with that price.
You therefore didn’t need to run, say, an opinion poll to find out market practitioners’ expectations about the volatility of share prices: you could infer their expectations from the prices of options. In the mid-1980s, the economists Menachem Brenner and Dan Galai began to lobby the US options exchanges to create a ‘volatility index’, based on options prices, that would measure stock-market volatility in a way loosely analogous to – albeit mathematically far more sophisticated than – how the Dow Jones average or the Standard & Poor 500 Index summarises the market’s overall level. By the early 1990s, the CBOE (the Chicago Board Options Exchange) was persuaded, and it commissioned the economist Robert Whaley to find the best way of constructing a volatility index covering the ensemble of stocks that made up the S&P 500.
The exact way in which the VIX – the CBOE Volatility Index – is calculated has changed over time, and its values have also been worked out retrospectively for the second half of the 1980s. (There’s no simple way of saying exactly what a given level of the VIX means. You may remember from school that a ‘standard deviation’ measures the amount by which, in aggregate, a characteristic such as people’s height varies from its average. The VIX is a sort of standard deviation, modified for the particularities of finance, and conceived of as measuring the variability of a single object – a price – that changes continually as time passes.) But what matters for us here is that the VIX did indeed begin as a gauge, as a measurement device: it wasn’t intended to affect the way options or shares were traded, and doesn’t so far seem to have done so to any great extent. It was never literally a fear gauge – the volatility of a price includes its upward as well as its downward movements – but traders have always looked to the VIX primarily to help them assess the extent to which investors, as an aggregate, are afraid of a major fall in prices.
The VIX became an intrinsic part of finance’s engine in 2004, when the Chicago Board Options Exchange turned it into something you could actually trade. The CBOE introduced futures contracts on the VIX, which allow traders to bet on or hedge themselves against coming rises or falls in the index. Those contracts, and the CBOE Futures Exchange on which they trade, are the pretty much exclusive domain of professional traders. In 2009-10, however, ‘exchange-traded funds’ (ETFs) linked to the VIX were made available. These are not futures contracts but shares that track the VIX, and although they are widely used by professionals, they are also easily accessed by amateur traders. The best-known of the ETFs is the VXX, which according to the Financial Times, was the fifth most heavily traded stock in the US in 2016. If the VXX isn’t racy enough for you, there’s also the UVXY, ranked number ten by trading volume. Buying the UVXY is the equivalent of trading the VIX using not just your own savings but also borrowed money.
The most interesting of all these ETFs is the one that traders know by its ticker symbol as the XIV. The XIV is an ‘inverse’ ETF: buying it is the equivalent of betting that volatility, as measured by the VIX, is going to continue to fall. That may seem esoteric, yet by last April, the XIV had climbed to 34th in the ranking of US shares by trading volume, surpassing blue-chip corporations such as Chevron and Pfizer. Actually it isn’t surprising that the XIV has been so successful: buying it is the most straightforward way for an amateur trader to bet that the VIX will fall, and, as the FT puts it, that bet ‘has trounced the returns of pretty much everything since the [banking] crisis’. Five years ago, you could buy a unit of the XIV for less than $20. In November 2017, you could have sold that unit for nearly $110, a return on your money of nearly 500 per cent. (Here perhaps we need a standard disclaimer: neither the author nor the LRB offers financial advice. Consult a professional!)
The ‘short VIX’ trade is the professionals’ term for the wager, made in recent years on a massive scale, that the VIX will go on falling. There are, of course, two parties to every trade, and the short VIX is no exception. The trading firms that accommodate giant bets on a falling VIX have to trade in such a way that they don’t lose money if the punters are right (so far, they usually have been right). The firms do that by entering into offsetting trades, which usually take the form – directly or indirectly – of trades on the CBOE in the options whose prices inform the calculation of the VIX. These trades increase the supply of these options, helping keep their prices, and thus the VIX, low. There is a feedback loop here, and it seems to have been operating on an industrial scale: so far, quite sufficient to keep the fear gauge seemingly stuck at the bottom of the scale, no matter what the antics of the occupant of the White House.
Many people in the markets suspect as I do that a loop of this kind explains why the VIX remains low. But we can’t be entirely sure. Things like this typically become clear only in retrospect, and sometimes not even then. I can’t even decide which prospect worries me more: that the ‘loop’ explanation is right or that it’s wrong. If it’s right, the danger comes from the fact that feedback loops in finance can suddenly fling themselves violently into reverse, causing severe whiplash. That was almost certainly an important element in the 1987 crash. Market participants who had ‘portfolio insurance’, an automatic hedging strategy based on the Black-Scholes-Merton model, needed to sell large numbers of S&P 500 futures contracts quickly, and that selling pressure fed through into the underlying stock market, contributing to a fall in share prices of around 20 per cent – the worst ever single day in the history of trading in the US. If all those market participants, amateur and professional, who are betting on the VIX remaining low were all at once to change their minds and try to liquidate their positions, a disruption on the scale of the 1987 crash couldn’t be ruled out.
Perhaps those betting that the VIX will remain low are aware of the feedback loop, and know that it could suddenly go into reverse: in other words, they are consciously taking a substantial risk in trying to squeeze the last few dollars out of what has been the best bet of the last five years. Or maybe they don’t perceive themselves to be taking a big risk, and the loop doesn’t explain why the fear gauge remains so low. Maybe investors in the US simply aren’t frightened. That would be worrying for different reasons. It reminds me of the widespread feeling, in the run-up to the global banking crisis, that markets were enjoying a durable ‘great moderation’, free of boom and bust, bubbles and crashes. The time for the rest of us to get scared is precisely when market participants aren’t.