Saturday, January 27, 2018

A Couple Thousand Words on the VIX at the London Review of Books

We stole the headline from Alexandra Scaggs at FT Alphaville's Further Reading post, January 25.

From the LRB:

Short Cuts
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....MORE
HT: Further Reading