risk

Risk = f(Volatility): A Generic Understanding of Volatility

Sanjeev

Sanjeev

In Classical Finance, Risk is synonymous with Volatility, and (believe it or not), mathematical/ statistical indicators of Volatility are used to denote measures of Risk. This is the miseducation that is propagated to our best minds in the B-Schools and goes on to become the received wisdom of the day.

Yet somehow, I don’t know why the growing numbers of “Risk Managers” that populate the ranks of the looking-for-work consultants or ex-CFOs, don’t pretend to be managers of Volatility. Is it because hardly anyone has taken the trouble to study the underlying causes of Volatility? Why is it that all these so-called “Forex Advisors” only want to be advisors, NOT managers of Forex Risk? Is it because they don’t want to take responsibility for the greatest measurable in Risk Management, the mark-to-market, i.e. MTM? The maximum possible MTM gives you the estimated Value-at-Risk, VaR. However, any trader must remember that he must stay alive through the worst day, hence his actual cash outlay will be the maximum MTM on the worst day.

Risk is no doubt derived from Volatility, but is not synonymous with it. Volatility is like the waves of the sea, its depth being similar to the MTMs you must suffer if you want to walk through the sea. But the Risk at sea is much more than the volatility of the waves. Let us understand the underlying causes of volatility:

  1. The first is randomness. Let me illustrate with an example. In a particular stock, a big seller may appear at 10 a.m. because that is the time (say) he gets into office. A big buyer may appear at (say) 2 p.m. because that is when he, well, buys….just after he gets back from lunch. For no particular reason, the stock drops 1% when the big sale happens to a lot of smaller buyers, and then it rises 1% when the big buy happens from a lot of smaller sellers.

Now, if each big transaction creates its own ‘momentum’, with a whole lot of technical analysts getting into and out of trades that are triggered by nothing but a big holder’s lunchtime, then you can imagine why all this ‘technical’ mumbo-jumbo usually goes nowhere. Far better to just humbly write all this down to noise, and to trade ‘statistically’, i.e. based on the simple statistical observation that markets go from one place to another in, say, 1.7% of the time and the remaining 98.3% of the time, they really go nowhere.

Yes, sir, that is right. Depending on how you define a cycle, you will find this approximate ratio across markets, from searing bull markets to deep bear markets.

  • Then there are the emotional “storm surges” that occur periodically. These look as random as the ‘timing mismatches’ above, but they do follow a pattern, which is discernible to the practicing Behavioural Economist. If you carry a lexicon of human irrationalities in your head, you will be able to identify these irrationalities when you see them…..sometimes you can even give them a clinical name. But yes, they do create volatility.

A good example of the “storm surge” was the Modi Mania in the first fortnight of May 2014 when currency and equity markets went through some manic moves, which have reversed somewhat, especially in the currency markets. In the currency markets, that was a Rupee-specific movement, as if the Dollar and other major counter-party currencies did not exist. In a short while, the focus shifted back to the rising Dollar (which, by the way, was already rising against everyone else, if you cared to look), and those who sold the Dollar at ~Rs.58.50 were left stranded. Predictable and very profitable volatility, for those who want to make this their vocation…

  • There is also a ‘trend volatility’ that has an underlying secular trend with many zigs and zags, rather like the Nifty has, since Sept 2013. As the market changes its mind, it ‘discovers’ a new trend in this zigzag sort of fashion. This kind of volatility is even more predictable.
  • Then there are these Black Swan events that appear like earthquakes on a chart. This happens when the crowd is shocked out of a consensus, like happened in the Nifty in the 3rd week of Jan 2008. These too are fairly predictable to contrarians and those who are able to keep minds sensible through a bull market.  

Remember that Risk is semantic, while Volatility is a mathematical term. Also, a lot of Risk is just perceived, not real…..and we have no way of telling the difference. Both real and perceived Risk creates the same kind of volatility, as investors head for the exits, whether the Cinema Hall is actually on fire or not. In an odd way, most real Risk falls under the Black Swan category; real damage happens only when investors have not anticipated sudden changes in fundamentals (the so-called AAA–rated mortgage-backed securities turning out to be junk in 2008, for example).

For example, let us take an example from the equity markets. In 2009-10, Bharti was beaten down from ~Rs.450 to a range of Rs.250-320. It stayed in that range for more than 3 years, while investors worked themselves into a tizzy over first the tariff wars, then the Zain acquisition, then the overhang of the 2G scam, then the launch of Reliance Jio and the overpayment for the 3G and spectrum renewals. All these hits were absorbed by the company and its financials still look pretty similar to what they were after the Zain acquisition. Think of the huge amount of daily volatility, the dying of a thousand deaths and the reams of newsprint spent on debating its destiny.

The actual “Risk” for an investor was not much, as the company absorbed all of these adversities in its stride and retained its valuation, but the number of zigs and zags certainly did much for its volatility. The stock has been a trader’s dream, vibrating in mostly the same place with no real damage.

A different kind of perceived Risk can be seen in the metal companies (say, Tata Steel and Hindalco) which have seen a lot of global scares about their respective international businesses, without much real damage. Right now, there is another cloud relating to their input mining and backward integration.

Perceived Risk is over the short term. Investors who look over a long-term horizon while the market looks at the short-term potential for volatility will be well rewarded. Look at the recent past, when there were fears that the Corus part would drag Tata Steel into near bankruptcy; instead, patient investors saw the Jamshedpur expansion come good, and EBIDTA is now running 20% higher. The stock is up 130% from the lows of last year. A similar overhang exists with fears of a China slowdown, but short-term investors are ignoring the fact that the Odisha plant that is coming on stream end-2014-15 is going to sharply increase valuations again. Meanwhile, an intrepid trader gets a near-guarantee that the company will eventually come out of the woods….and that today’s perceived Risk is wrong over the long term.    

The point I am making is that Risk is not the same as Volatility. A large part of Volatility is actually an opportunity, which actually reduces long-term Risk for a business or a portfolio. If we understand and segregate the underlying drivers of volatility outlined above, we will find that only Black Swan volatility represents real Risk; all the other drivers are closer to perceived Risk. If understood well, they are opportunities to reduce the real Risk. 

Mathematicians/ statisticians will reduce Volatility to different categories, or line them (candlesticks representing short-term daily volatility) up in a Bell Curve. At this point, Volatility lends itself to manipulation that Risk does not.  Risk is the probability of actual loss, and by that, I presume they mean long-term loss. Volatility is more a measure of uncertainty than of Risk. But to the human mind, uncertainty represents the fear of the unknown, which is irrationally confused with the probability of disaster, hence Risk.

So once you have understood this, what is the probability that we can predict ‘implied volatility”, i.e. look into the future and try and predict uncertainty, i.e. reduce it to a good-looking Bell curve, which everybody understands, but which is plain wrong.   

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