Volatility: Meaning In Finance and How it Works with Stocks

What Is Volatility?

Volatility is a statistical measure of the dispersion of returns for a given security or market index. In most cases, the higher the volatility, the riskier the security. Volatility is often measured from either the standard deviation or variance between returns from that same security or market index.

In the securities markets, volatility is often associated with big swings in either direction. For example, when the stock market rises and falls more than one percent over a sustained period of time, it is called a volatile market. An asset's volatility is a key factor when pricing options contracts.

Understanding Volatility

Volatility often refers to the amount of uncertainty or risk related to the size of changes in a security's value. A higher volatility means that a security's value can potentially be spread out over a larger range of values. This means that the price of the security can change dramatically over a short time period in either direction. A lower volatility means that a security's value does not fluctuate dramatically, and tends to be more steady.

One way to measure an asset's variation is to quantify the daily returns (percent move on a daily basis) of the asset. Historical volatility is based on historical prices and represents the degree of variability in the returns of an asset. This number is without a unit and is expressed as a percentage.

While variance captures the dispersion of returns around the mean of an asset in general, volatility is a measure of that variance bounded by a specific period of time. Thus, we can report daily volatility, weekly, monthly, or annualized volatility. It is, therefore, useful to think of volatility as the annualized standard deviation.

How to Calculate Volatility

Volatility is often calculated using variance and standard deviation (the standard deviation is the square root of the variance). Since volatility describes changes over a specific period of time, you simply take the standard deviation and multiply that by the square root of the number of periods in question:

Volatility = σ√T

where:

• σ =standard deviation of returns
• T = number of periods in the time horizon

For simplicity, let's assume we have monthly stock closing prices of $1 through $10. For example, month one is $1, month two is $2, and so on. To calculate variance, follow the five steps below.

1. Find the mean of the data set. This means adding each value and then dividing it by the number of values. If we add, $1, plus $2, plus $3, all the way to up to $10, we get $55. This is divided by 10 because we have 10 numbers in our data set. This provides a mean, or average price, of $5.50.
2. Calculate the difference between each data value and the mean. This is often called deviation. For example, we take $10 - $5.50 = $4.50, then $9 - $5.50 = $3.50. This continues all the way down to the first data value of $1. Negative numbers are allowed. Since we need each value, these calculations are frequently done in a spreadsheet.
3. Square the deviations. This will eliminate negative values.
4. Add the squared deviations together. In our example, this equals 82.5.
5. Divide the sum of the squared deviations (82.5) by the number of data values.

In this case, the resulting variance is $8.25. The square root is taken to get the standard deviation. This equals $2.87. This is a measure of risk and shows how values are spread out around the average price. It gives traders an idea of how far the price may deviate from the average.

If prices are randomly sampled from a normal distribution, then about 68% of all data values will fall within one standard deviation. Ninety-five percent of data values will fall within two standard deviations (2 x 2.87 in our example), and 99.7% of all values will fall within three standard deviations (3 x 2.87).

In this case, the values of $1 to $10 are not randomly distributed on a bell curve; rather. they are uniformly distributed. Therefore, the expected 68%–95%–99.7% percentages do not hold. Despite this limitation, traders frequently use standard deviation, as price returns data sets often resemble more of a normal (bell curve) distribution than in the given example.

Types of Volatility

Implied Volatility

Implied volatility (IV), also known as projected volatility, is one of the most important metrics for options traders. As the name suggests, it allows them to make a determination of just how volatile the market will be going forward. This concept also gives traders a way to calculate probability. One important point to note is that it shouldn't be considered science, so it doesn't provide a forecast of how the market will move in the future.

Unlike historical volatility, implied volatility comes from the price of an option itself and represents volatility expectations for the future. Because it is implied, traders cannot use past performance as an indicator of future performance. Instead, they have to estimate the potential of the option in the market.

Historical Volatility

Also referred to as statistical volatility, historical volatility (HV) gauges the fluctuations of underlying securities by measuring price changes over predetermined periods of time. It is the less prevalent metric compared with implied volatility because it isn't forward-looking.

When there is a rise in historical volatility, a security's price will also move more than normal. At this time, there is an expectation that something will or has changed. If the historical volatility is dropping, on the other hand, it means any uncertainty has been eliminated, so things return to the way they were.

This calculation may be based on intraday changes, but often measures movements based on the change from one closing price to the next. Depending on the intended duration of the options trade, historical volatility can be measured in increments ranging anywhere from 10 to 180 trading days.

Volatility and Options Pricing

Volatility is a key variable in options pricing models, estimating the extent to which the return of the underlying asset will fluctuate between now and the option's expiration. Volatility, as expressed as a percentage coefficient within option-pricing formulas, arises from daily trading activities. How volatility is measured will affect the value of the coefficient used.

Volatility is also used to price options contracts using models like Black-Scholes or binomial tree models. More volatile underlying assets will translate to higher options premiums because with volatility there is a greater probability that the options will end up in-the-money at expiration. Options traders try to predict an asset's future volatility, so the price of an option in the market reflects its implied volatility.

Other Measures of Volatility

Beta

One measure of the relative volatility of a particular stock to the market is its beta (β). A beta approximates the overall volatility of a security's returns against the returns of a relevant benchmark (usually the S&P 500 is used). For example, a stock with a beta value of 1.1 has moved 110% for every 100% move in the benchmark, based on price level.

Conversely, a stock with a beta of .9 has moved 90% for every 100% move in the underlying index.

The VIX

Market volatility can also be seen through the Volatility Index (VIX), a numeric measure of broad market volatility. The VIX was created by the Chicago Board Options Exchange as a measure to gauge the 30-day expected volatility of the U.S. stock market derived from real-time quote prices of S&P 500 call-and-put options. It is effectively a gauge of future bets investors and traders are making on the direction of the markets or individual securities. A high reading on the VIX implies a risky market.

Traders can also trade the VIX using a variety of options and exchange-traded products, or they can use VIX values to price certain derivative products.

Tips on Managing Volatility

Investors can find periods of high volatility to be distressing as prices can swing wildly or fall suddenly. Long-term investors are best advised to ignore periods of short-term volatility and stay the course. This is because over the long run, stock markets tend to rise. Meanwhile, emotions like fear and greed, which can become amplified in volatility markets, can undermine your long-term strategy. Some investors can also use volatility as an opportunity to add to their portfolios by buying the dips, when prices are relatively cheap.

You can also use hedging strategies to navigate volatility, such as buying protective puts to limit downside losses without having to sell any shares. But note that put options will also become more pricey when volatility is higher.

Example of Volatility

Suppose that an investor is building a retirement portfolio. Since she is retiring within the next few years, she's seeking stocks with low volatility and steady returns. She considers two companies:

1. ABC Corp. has a beta coefficient of .78, which makes it slightly less volatile than the S&P 500 index.
2. XYZ, Inc. has a beta coefficient of 1.45, making it significantly more volatile than the S&P 500 index.

A more conservative investor may choose ABC Corp. for its portfolio, since it has less volatility and more predictable short-term value

The Bottom Line

Volatility is how much and how quickly prices move over a given span of time. In the stock market, increased volatility is often a sign of fear and uncertainty among investors. This is why the VIX volatility index is sometimes called the "fear index." At the same time, volatility can create opportunities for day traders to enter and exit positions. Volatility is also a key component in options pricing and trading.