What Is Volatility?
Volatility serves as a statistical indicator that gauges the extent of return variability within a specific security or market index. Typically, greater volatility indicates heightened risk within the security. This measure is commonly derived from either the standard deviation or variance among returns of the identical security or market index.
In financial contexts, volatility frequently links with significant fluctuations in either upward or downward directions. For instance, if the stock market experiences consistent movements of over one percent in its value, it qualifies as a volatile market. When determining the value of options contracts, the volatility of an asset plays a crucial role.
- Volatility illustrates the extent of price fluctuations of an asset relative to its average price—this measurement captures the spread of its return variations.
- Various methods exist for gauging volatility, such as beta coefficients, models for pricing options, and calculating the standard deviations of returns.
- Assets with high volatility are commonly viewed as riskier compared to those with lower volatility, as their prices are anticipated to be less foreseeable.
- Implied volatility evaluates the expected market volatility, while historical volatility assesses price shifts during specific pre-defined time spans.
- Volatility stands as a significant factor in the computation of options’ values.
Concept of Volatility
Volatility commonly refers to the level of uncertainty or risk connected to the magnitude of changes in the value of a security. A higher volatility suggests that the value of a security can potentially cover a broader range of values. Consequently, the security’s price can undergo significant changes over a short period in either direction. On the other hand, lower volatility indicates that the value of a security remains relatively stable without major fluctuations.
One method to gauge an asset’s variability involves measuring its daily returns (percentage change on a daily basis). Historical volatility, which relies on past prices, reflects the extent of fluctuations in an asset’s returns. This figure is dimensionless and is presented as a percentage.
Although variance captures the extent of return dispersion around an asset’s average value, volatility represents that variance within a specific timeframe. This enables us to report daily, weekly, monthly, or annualized volatility. Therefore, it’s helpful to envision volatility as the annualized standard deviation.
How to Calculate Volatility
Volatility is frequently computed using variance and standard deviation (where the standard deviation is the square root of the variance). As volatility depicts changes within a certain timeframe, you merely need to take the standard deviation and multiply it by the square root of the relevant number of periods.
vol = σ√T
- v = volatility over some interval of time
- σ =standard deviation of returns
- T = number of periods in the time horizon
To keep things simple, let’s consider a scenario where we possess monthly closing stock prices ranging from $1 to $10. Just as an illustration, the first month’s price is $1, the second month’s price is $2, and so forth. To compute variance, adhere to the following five steps.
- Determine the average of the dataset. To achieve this, sum up all the values and then divide the total by the number of values. For instance, if we add up $1, $2, $3, all the way to $10, the sum is $55. Dividing this by 10 (since there are 10 values) yields an average, or mean price, of $5.50.
- Compute the difference between each data value and the average. This is often termed as the deviation. As an illustration, take $10 – $5.50 = $4.50, followed by $9 – $5.50 = $3.50, and so on until you reach the first value of $1. Negative numbers are acceptable. These calculations are usually carried out in a spreadsheet for precision.
- Square the deviations. This operation eliminates negative values.
- Sum up the squared deviations. In our example, this amounts to 82.5.
- Divide the total of squared deviations (82.5) by the number of data values.
In this instance, the outcome is a variance of $8.25. To obtain the standard deviation, we take the square root of this value, resulting in $2.87. This serves as a risk measurement, indicating the extent to which values disperse from the average price. It offers traders insight into the potential divergence of the price from the average.
If prices are chosen at random from a normal distribution, approximately 68% of the data points will lie within one standard deviation. Around 95% of the data points will be within two standard deviations (2 x 2.87 in our instance), and about 99.7% of all values will fall within three standard deviations (3 x 2.87).
However, when considering values from $1 to $10, they aren’t distributed in a bell curve manner; instead, they follow a uniform distribution. As a result, the anticipated percentages of 68%–95%–99.7% do not apply in this context. Still, traders often make use of standard deviation because price return datasets frequently exhibit a distribution more akin to a normal (bell curve) distribution than the example provided.
Tip: It is believed that the fluctuation in stock prices tends to balance out over time. This means that when there’s high volatility, it’s likely to decrease, and when there’s low volatility, it’s likely to increase, all centered around a stable average.
Types of Volatility
Implied volatility (IV), also referred to as anticipated volatility, stands as a crucial measure for traders dealing in options. Its purpose lies in enabling them to assess the probable level of market volatility in upcoming times. This concept also furnishes traders with a method to compute likelihood. It’s important to highlight that this is not a precise science and thus doesn’t offer a prediction about future market movements.
Differing from historical volatility, implied volatility is derived from an option’s price itself and signifies the anticipated volatility for the future. Because it’s inferred, traders cannot rely on past performance to gauge future results. Instead, they must gauge the potential of the option within the market.
Note: Implied volatility is a key feature of options trading.
Also known as statistical volatility, historical volatility (HV) assesses the fluctuations in underlying securities by analyzing price shifts over predetermined time spans. It’s a less commonly used measure compared to implied volatility, as it doesn’t predict future outcomes.
When historical volatility increases, the price of a security experiences more significant movements than usual. This indicates an anticipation or occurrence of change. Conversely, a decrease in historical volatility suggests a reduction in uncertainty, leading to a return to previous conditions.
This calculation might involve intraday variations, but often quantifies movements based on the shift between one closing price and the next. Depending on the desired duration of the options trade, historical volatility can be evaluated over periods ranging from 10 to 180 trading days.
Volatility and Options Pricing
Volatility stands as a crucial factor in options pricing models, predicting the degree to which the underlying asset’s returns will shift between now and the option’s expiry. This percentage-based coefficient, denoting volatility within option pricing formulas, stems from daily trade actions. The manner in which volatility is gauged influences the value of the coefficient in use.
Furthermore, volatility plays a role in pricing options contracts through methods such as Black-Scholes or binomial tree models. Assets with higher volatility lead to elevated options premiums since heightened volatility corresponds to an increased likelihood of options ending up in-the-money at expiry. Options traders aim to foresee an asset’s forthcoming volatility, causing an option’s market price to reflect its implied volatility.
Important: Increased volatility results in elevated market prices for options contracts universally.
Other Measures of Volatility
A way to gauge how much a specific stock’s volatility compares to the market is by using its beta (β). Beta estimates the general volatility of a security’s returns when measured against the returns of a suitable benchmark (typically the S&P 500). For instance, a stock with a beta of 1.1 has historically shown a movement of 110% for every 100% shift in the benchmark’s value.
On the other hand, a stock with a beta of 0.9 has historically moved 90% for every 100% alteration in the underlying index.
Market volatility can also be observed through the Volatility Index (VIX), a numerical indicator of overall market volatility. Developed by the Chicago Board Options Exchange, the VIX serves as a tool to assess the anticipated 30-day volatility of the U.S. stock market, based on real-time quote prices of S&P 500 call and put options. It essentially represents the future predictions that investors and traders are placing on market directions and individual securities. A higher VIX reading indicates a more uncertain market environment.
Traders have the opportunity to engage with the VIX through various options and exchange-traded products. Additionally, they can employ VIX values to determine the pricing of specific derivative products.
Tips on Managing Volatility
Investors might find periods of high volatility concerning due to the potential for significant price swings or sudden drops. However, it’s important for long-term investors to overlook short-term volatility and stay committed to their strategy. This approach is justified by the historical tendency of stock markets to rise over extended periods. Strong emotions like fear and greed, which can intensify during volatile times, have the potential to disrupt long-term investment plans. For some investors, market volatility presents a chance to enhance their portfolios by purchasing stocks during price declines, when they’re relatively inexpensive.
Hedging strategies can also be employed to navigate volatility, like acquiring protective put options to limit potential losses without needing to sell shares. Keep in mind that the cost of put options will also increase when volatility is elevated.
Example of Volatility
Imagine an investor who is assembling a retirement portfolio. As she’s approaching retirement in the coming years, she’s on the lookout for stocks that offer stable returns and minimal fluctuations in value. She evaluates two companies:
- ABC Corp. possesses a beta value of .78, indicating it’s slightly less volatile compared to the S&P 500 index.
- On the other hand, XYZ, Inc. holds a beta coefficient of 1.45, signifying it’s notably more volatile than the S&P 500 index.
An investor who prefers a cautious approach might opt for ABC Corp. in their portfolio due to its lower volatility and more foreseeable short-term value.
How Can Volatility Be Described Mathematically?
Volatility is a statistical way to gauge how data spreads out from its average within a specific timeframe. It’s figured out by multiplying the standard deviation by the square root of the time period, denoted as T. In the realm of finance, it mirrors the extent of price variation in the market, usually on an annualized scale.
Does Volatility Equate to Risk?
Volatility is commonly used to depict risk, yet they aren’t always identical. Risk pertains to the possibility of encountering losses, while volatility relates to the extent and speed of price changes. If heightened price fluctuations also amplify the likelihood of losses, then risk is also heightened.
Is Volatility Beneficial?
Whether volatility is advantageous or unfavorable hinges on the type of trader you are and your willingness to take risks. For those with long-term investment goals, volatility can pose challenges, while day traders and options traders often view volatility as a chance for trading activities.
What Does a High Volatility Mean?
What Does the VIX Refer To?
The VIX stands for the CBOE volatility index, which gauges the immediate volatility within the overall market. This is determined by the expected volatility of S&P 500 options contracts over a 30-day span. Typically, the VIX increases when stocks decrease and decreases when stocks rise. Referred to as the “fear index,” the VIX serves as an indicator of market sentiment, where higher values imply increased volatility and heightened apprehension among investors.
Also Read: Options Trading for Beginners