INTRODUCTION TO VOLATILITY

By David Landry

Traders are never far from the concept of volatility--either in the markets or on the news. We hear about it all the time: Day traders are advised that volatility is their best friend when it comes to intraday trading opportunities, while long-term investors are forever warned to hold tight and weather the most recent period of volatility until things settle down again. It's no wonder many traders have trouble understanding what volatility really means and how it affects their trading.

To better understand this crucial aspect of trading, first we will look at what volatility represents, its inherent features and a simple way of measuring it. We'll also look at general ways of applying these concepts to the markets. In future articles, we’ll look at more complex volatility measurements and more specific trading techniques.

A Simple Concept

From a mathematical standpoint, volatility is one of the more complex market concepts--but that doesn’t mean it has to be difficult to understand in practical trading terms. Volatility is simply how much prices change over a given period of time. For instance, if the Dow Jones Industrial Average goes up 10 points one day and down 10 points the next you would probably say volatility is low. However, if it goes up 200 points one day and down 200 points the next, then you'd probably say the market is volatile.

In the most basic sense, that's really all there is to it. The more complex stuff has to do with measuring volatility consistently, tracking its behavior, and taking advantage of its characteristics.

Volatility Characteristics

Volatility has certain inherent features: cyclicity, persistency and mean reversion. Although they might initially sound intimidating, again, the concepts are actually quite simple.

Volatility is cyclical: Volatility tends to run in cycles, increasing and peaking out, then decreasing until it bottoms out and begins the process all over again. Many traders believe volatility is more predictable than price (because of this cyclical characteristic) and have developed models to capitalize on this phenomena.

Volatility is persistent: Persistency is simply the ability of volatility to follow through from one day to the next, suggesting the volatility that exists today will likely to exist tomorrow. That is, if the market is highly volatile today, it will most likely be volatile tomorrow; conversely, if the market not volatile today it will likely not be volatile tomorrow. By the same token, if volatility is increasing today, it will likely continue to increase tomorrow, and if volatility is decreasing today, it will likely continue to decrease tomorrow.

Volatility tends to revert to the mean: Someone once asked me to describe reversion to the mean (average) in as simple terms as possible. My reply was if you know someone who’s normally “mean” and then their nice to you for a few days, chances are they’ll revert back to being mean.

Seriously, this concept simply means that volatility has a tendency to revert back to more average or normal levels when it reaches a high or low extreme. Once a market hits an extreme high in volatility, it will likely revert back to the mean--that is, volatility will fall back to more normal or average levels. Conversely, once volatility hits an extremely low level, it will likely rise to more normal (or average) levels. It’s like a rubber band: when stretched so far, it tends to snap back.

Figure 1. Volatility characteristics

The above concepts are illustrated in Figure 1. Notice the cyclical characteristic of volatility. It tends to oscillate back and forth between periods of low volatility and periods of high volatility. It tends to persist (follow through). Days of increasing volatility (a) tend to be followed by days of increasing volatility (b). Conversely, days of decreasing volatility (c) tend to be followed by days of decreasing volatility (d). Finally, it tends to revert back to its mean--that is, periods of extremely high volatility (e) tend to be followed by moves to more normal or average levels (f). Conversely, periods of extremely low volatility (g) tend to be followed by periods of more normal or average volatility (h).

Measuring Volatility

Because this is a an introductory article on volatility, we’ll show a simple way to measure it. One of the easiest ways is to take the average range (high – low) over a given period. The number of days (or hours, or weeks, etc.) you use in your calculation will give you a picture of the volatility over that time period. A five-day average range calculation will give you an idea of how volatile the market has been the past week, but it won't tell you anything about the past six months. A 100-day average range calculation would reflect volatility over a much longer period.

Figure 2. True range.

Because more volatile markets often gap higher or lower overnight, the true range, developed by Welles Wilder, provides a more accurate measurement of volatility because it accounts for overnight gaps in its calculation. This concept is illustrated in Figure 2. Because the range for only one day doesn’t provide much information, the true range can be averaged over a period of time (say two weeks). This average true range gives you a better feel for volatility over time.

True range is the largest value (in absolute terms) of:

1. today’s high and today’s low
2. today’s high and yesterday’s close
3. today’s low and yesterday’s close

Figure 3. Global Telesystems (GTSG) Source: Omega Research.

Here we measured volatility by taking the 10-day average true range (ATR). Again, notice the cyclical nature of volatility. It tends to cycle from periods of high volatility to periods of low volatility. It tends to persist, periods of increasing volatility (a) tend to be followed by periods of increasing volatility (b). Conversely, periods of decreasing volatility (c) tend to be followed by periods of decreasing volatility (d). Also, notice that it tends to revert back to its mean. That is, periods of extremely low volatility (e) tend to be followed by higher or more normal (average) levels of volatility (f). Conversely, periods of high volatility (g) tend to be followed by periods of lower or more normal or average (h) levels of volatility.

General trading applications

Higher volatility markets offer potentially larger profits accompanied by increased risk. Short-term traders, whose profits are limited by how much a stock or futures contract can move in a given amount of time, may seek more volatile markets. Longer-term or more conservative investors may seek markets that are less volatile.

If the volatility of a market is extremely low (compared to average or normal levels), then chances are a larger move is imminent as volatility reverts to its mean. Conversely, if volatility is extremely high (compared to normal levels) then the large price move which created the jump in volatility may be over as volatility reverts back to more normal levels.

Summing Up

Volatility measures the changes in price of a market over a given time period. The average true range of a market provides a simple way of calculating volatility. Markets that are generally volatile offer potentially larger profits with the trade off of increased risk. Volatility has a few important characteristics: cyclicity, persistency and reversion to the mean. These concepts can be used to help determine which markets offer the highest potential for profits, when a large move is likely to occur and when the move may be over.

In parts two and three of this series, we’ll expand upon these concepts using historical volatility, a more mathematically complex but useful way of measuring volatility. We’ll show how it can be used to find (or avoid) highly volatile markets, determine realistic points to set initial protective stops and to find markets that are likely to explode or enter a low-volatility congestion period.

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