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The Chartist: Volatile Facts

Fear Gauge or the Volatility Index shows that premium volatility tends to be low close to market peaks and high close to market bottoms

The Chicago Board Option Exchange (CBOE) has an index nicknamed the “Fear Gauge”. This Volatility Index (VIX) consists of a compilation and assessment of the volatility of the premiums of all puts and calls on the S&P500, a broad index that tracks 500 US-listed stocks.

Launched in 1993, the VIX has proven utility as a traders’ tool. In a nutshell, the VIX shows that premium volatility tends to be low close to market peaks (due to complacency) and high (because of panic) close to market bottoms.

This has obvious implications for traders and interesting and far-reaching implications for behavioural scientists. Option premium volatility mirrors the expectations of volatility (implied volatility or IV) in the prices of the underlying. The IV seems to be always higher during a downtrend or at the bottom of a market than at the peak. This appears to be true across a whole range of instruments and markets. The CBOE also tracks non-US instruments through similar volatility indices, which display similar behaviour for Tokyo, London, Singapore, etc. India doesn't have an options market with a great deal of history but this behaviour appears to be true for the Nifty's puts and calls as well. Premiums change quicker during a downtrend than during an uptrend.

Thus, the VIX is a contrarian tool. It has an inverse relationship with market direction. Over the years, traders have built up complex models that help them trade it with a degree of consistency.

Now what about the volatility of the underlying? Well, this too appears to increase during a downtrend. Can we develop simple trading rules of thumb that help us to call the direction of an index for example, the Nifty?

The calculations for a comparable Nifty options volatility index would be fairly complicated. While the data is easily available, a fair amount of massaging would be required. In contrast, a simple back of the envelope calculation could give us enough information to make a call on direction.

One of the simplest measures of volatility is the high-low range of an index, expressed as a percentage of the closing price. We can easily derive a mean as well as a median value for this percentage time-series. Using either the mean or the median as a baseline, we can easily check whether volatility is running unusually high or low. By mapping that volatility across historical price-trend, we can judge if it has an inverse relationship. If that is true, we can fine-tune as required to develop trading models.

The advantage to this is that it can be done real-time with a quick mental calculation and that could mean a big edge if it enables a trader to be early into a new trend. That additional speed could prove to be a bigger edge than spot-on accuracy, which is anyhow a pipe-dream when trading financial instruments. Here's how one could develop a rule of thumb. Over the past 10 years, the Nifty's average high-low range has been about 2.1% of its closing price and the median has been around 1.77 per cent. During this period, there have 1266 sessions when the Nifty gained versus the previous close and 1059 sessions when the Nifty lost ground. By definition, the market has seen a higher range than the median on 1162 occasions and less movement on 1162 occasions. . For a rigorous mathematical calculation, the median may be more useful because it defines the half-way point. However, for a trader who wants a rule of thumb, it is probably better to work with the amplified signals that arise from the average. Very large changes in daily closing prices are always associated with range movements that are way beyond the norm. The really big swings in terms are associated with the days when prices have dropped dramatically. Quite interestingly, the periods when the market has downtrended (as in lost ground over several successive sessions) have always seen amplified ranges. That is, if the market has seen three unusually high ranging movements in the past five sessions, it is likely to establish a downtrend that will last for another five sessions or so. So a trader who sees say two sessions of high-low ranges larger than 2.5 per cent movement would be able to go short with a degree of confidence.

We can fine tune this. For example, it can be smoothened to reflect recent trends movements by taking for example a 200 day Moving average median and comparing that to say the past five sessions’ MA. That could give us a technical trading system that throws up automatic signals on an easily programmed worksheet macro.

We can also relate this sort of volatility to price differentials between markets. For example, when range volatility is high, the differences between futures and spot market prices also tend to rise. Again, this gives a smart trader leads to develop new models. Unfortunately one doesn't have the space in a general publication of this sort to illustrate the complexities of this sort of trading model. But I hope this will be enough to interest the technically-inclined trader.