Volatility – virus, bubble, or both?

7 min read 9 Feb 18

Summary: This latest ‘flash crash’ is a notable event. There have been only three phases in last 25 years when the S&P500 has moved this rapidly in this short a period of time, a fact drawn to my attention by my perceptive colleague, Marc Beckenstrater (see figure 1). Similar moves in the last 25 years have coincided with genuine events. The Asian crisis, the tech bust, and the GFC. This latest flash crash is distinct – it has occurred in the absence of news. So what is going on?

The volatility virus

When I get asked by clients what the biggest bubble out there is, I usually point to ‘volatility’. Not low volatility or high volatility. But the very concept of volatility. Value at risk (VAR) analysis, volatility-based measures of active risk, volatility targeting, risk-parity – these fads have hijacked the collective consciousness.

Thirty years ago, only a small minority of professional investors or commentators spoke about volatility (the Vix index itself was launched in 1993). To the extent that the majority had much interest in the subject it was akin to Warren Buffett’s famous maxim about the dangers of following any investment advice that involved the use of the Greek alphabet.

Despite what was happening in finance departments throughout academia, practitioners thought very differently about risk. Almost all the great investors had highlighted swings in market prices as sources of opportunity. Ben Graham famously describes the manic depressive Mr. Market as a servant to the intelligent investor with a long time horizon. Risk was definitively not the variance of prices, or even extreme moves, but permanent or sustained losses. Keynes described optimal portfolio construction in these terms in the 1930s: the key to good investing was to avoid what he termed a ‘stumer’ – a stock whose value was permanently destroyed.

Today, volatility-based frameworks are omnipresent. Portfolio risk is measured using VAR models, which are attempts to capture the volatility of a portfolio, and specify probable losses with varying degrees of statistical confidence, subject to assumptions about correlations and return distributions. Investors everywhere are being encouraged to define their ‘risk profile’. This translates into constructing portfolios which attempt to target levels of volatility – the risk averse elderly are encouraged to invest in less volatile funds, the risk-taking young professionals are encouraged to move higher up the volatility spectrum.

Every corner of the professional investment industry globally is obsessed with volatility. Risk managers base their processes around volatility, institutional investors want volatility targets, the private sector and the regulator are using volatility as the lens through which all is captured.

The reasons for this are understandable. Volatility – measured as the standard deviation of a security price – is easy to measure. And measurement is the holy grail. If you can measure, you can compute.

But there are three significant problems:

1. Investor behaviour is becoming more correlated

The first problem of which is typically unrecognised: investor behaviour is becoming correlated. That’s jargon for saying more people are behaving in exactly the same way. This is an argument that was put forward by John Authers in his book ‘The Fearful Rise of Markets’ and is suggested by Andrew Lo’s Adaptive Markets Hypothesis.

The correlation of beliefs and behaviour is one of the most compelling explanations as to why asset prices frequently move by far more than is warranted by changes in underlying fundamental news. This has been formalised in the work of Stanford University’s Mordecai Kurz, perhaps the most underrated innovator in financial theory of the last 50-years.

The intuition is relatively straightforward. If investors are different, have different objectives, preferences, and beliefs about the future, there is a higher probability of orderly trade – buyers will easily find sellers and vice-versa. But if behaviour is correlated, and everyone attempts to move in one way it will require great price moves for the market to clear. Correlated behaviour accentuates price moves.

2. Measurement and vast quantities of data, are a recipe for pseudo-science and over-confidence.

Technology has played an important role as carrier of this virus. Technology creates an incentive to quantify. That is the fundamental appeal of a statistical, price-based measure of risk. We have vast quantities of data, we can compare all portfolios, and apply limitless computing power. So it is not a surprise that the volatility virus has spread to infect all corners of investment markets and is a global phenomenon. Technology amplifies our hardwiring – we can copy and compare ourselves to everyone else. The greatest statisticians in finance – from Keynes, to Markowitz, to Fama, to Taleb (who has strongly attacked the use of VAR in particular), emphasise how little we know. Statisticians in finance should be extremely humble.

3. Volatility is not risk

Thirdly, volatility is not risk. Volatility is a measure of short term fluctuations in value, whereas the only true risk ultimately in the long term is that of permanent loss of capital. Volatility is a poor proxy, much of the time, for permanent loss of capital, and ergo a poor measure of risk.

Certainly, measuring volatility using daily prices and three month trailing sample periods – which underpins the famous Vix index- should be no one’s measure of risk, other than a leveraged Vix trader. Investors are not supposed to have daily time horizons, and three months is a spurious sample period for anyone with a three to five-year investment horizon. Most investors should be thinking at least in terms of five year horizons, if not decades. Risk, fundamentally, is not a number – or at least not solely a number.


Is this a bubble? It is a bubble of sorts. It may in part explain why cash rates globally are so low. Cash has zero nominal volatility. It also has a near guaranteed real loss across the entire developed world. Is something safe if it has no volatility but always loses money? It can be in a VAR model.

The latest flash crash may indicate the size of this bubble. Gavin Jackson of the Financial Times reminds us that it is right to be sceptical that anything genuinely novel is at play when markets crash, so does Clifford Asness. The stock market’s propensity to crash is as old as the stock market.

But my hunch, and it can be little more than that, is that we are observing something more pernicious. Endogenous instability is rising, and volatility is at the core. Volatility has virus-like properties. It started as the domain of a small specialist group of quants. And it is has spread to infect everyone. It propagates because we can compare the volatility of diverse funds, we can measure it objectively, and – as is frequently pointed out to me – what is the alternative?

Technology is not constraining behavioural biases, it is amplifying them. Wild swings in markets have always been defined by myopia. It explains why markets trend higher, and often move up exponentially. It also explains crashes. The most well-established observation from behavioural finance is the concept of myopic loss aversion. This is jargon for what thoughtful market participants have always observed – that humans have a very strong propensity to sacrifice long-term returns to avoid the pain of short term loss. All sound investment advice, at least since the onset of capitalism, if not prior, recognises the returns to patience.

It is a great irony that the volatility bubble is now creating volatility. How will this bubble burst, if it ever does?


By Eric Lonergan

The value of investments will fluctuate, which will cause prices to fall as well as rise and you may not get back the original amount you invested. Past performance is not a guide to future performance.

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