In short: The ** Volatility** or

*index measures the price swing in the markets for the next 30 days by looking at the price of options.*

**Fear**What Is the CBOE Volatility Index (VIX)?

Created by the Chicago Board Options Exchange (CBOE), the Volatility Index, or VIX, is a real-time market index that represents the market’s expectation of 30-day forward-looking volatility. Derived from the price inputs of the S&P 500 index options, it provides a measure of market risk and investors’ sentiments. It is also known by other names like “Fear Gauge” or “Fear Index.” Investors, research analysts and portfolio managers look to VIX values as a way to measure market risk, fear and stress before they take investment decisions.

## How Does the VIX Work?

For financial instruments like stocks, volatility is a statistical measure of the degree of variation in their trading price observed over a period of time. On 27 September 2018, shares of Texas Instruments Inc. (TXN) and Eli Lilly & Co. (LLY) closed around similar price levels of $107.29 and $106.89 per share, respectively. However, a look at their price movements over the past one month (September) indicates that TXN (Blue Graph) had much wider price swings compared to that of LLY (Orange Graph). TXN had higher volatility compared to LLY over the one-month period.

Extending the observation period to last three months (July to September) reverses the trend: LLY had much wider range for price swings compared to that of TXN, which is completely different from the earlier observation made over one month. LLY had higher volatility than TXN during the three month period.

Volatility attempts to measure such magnitude of price movements that a financial instrument experiences over a certain period of time. The more dramatic the price swings are in that instrument, the higher the level of volatility, and vice versa.

### How Volatility is Measured

Volatility can be measured using two different methods. First is based on performing statistical calculations on the historical prices over a specific time period. This process involves computing various statistical numbers, like mean (average), variance and finally the standard deviation on the historical price data sets. The resulting value of standard deviation is a measure of risk or volatility. In spreadsheet programs like MS Excel, it can be directly computed using the STDEVP() function applied on the range of stock prices. However, standard deviation method is based on lots of assumptions and may not be an accurate measure of volatility. Since it is based on past prices, the resulting figure is called “realized volatility” or “historical volatility (HV).” To predict future volatility for the next X months, **a **commonly followed approach is to calculate it for the past recent X months and expect that the same pattern will follow.

The second method to measure volatility involves inferring its value as implied by option prices. Options are derivative instruments whose price depends upon the probability of a particular stock’s current price moving enough to reach a particular level (called the strike price or exercise price). For example, say IBM stock is currently trading at a price of $151 per share. There is a call option on IBM with a strike price of $160 and has one month to expiry. The price of such a call option will depend upon the market perceived probability of IBM stock price moving from current level of $151 to above the strike price of $160 within the one month remaining to expiry. Since the possibility of such price moves happening within the given time frame are represented by the volatility factor, various option pricing methods (like Black Scholes model) include volatility as an integral input parameter. Since option prices are available in the open market, they can be used to derive the volatility of the underlying security (IBM stock in this case). Such volatility, as implied by or inferred from market prices, is called forward looking “implied volatility (IV).”

Though none of the methods is perfect as both have their own pros and cons as well as varying underlying assumptions, they both give similar results for volatility calculation that lie in a close range.

### Extending Volatility to Market Level

In the world of investments, volatility is an indicator of how big (or small) moves a stock price, a sector-specific index, or a market-level index makes, and it represents how much risk is associated with the particular security, sector or market. The above stock-specific example of TXN and LLY can be extended to sector-level or market-level. If the same observation is applied on the price moves of a sector-specific index, say the NASDAQ Bank Index (BANK) which comprises of more than 300 banking and financial services stocks, one can assess the realized volatility of the overall banking sector. Extending it to the price observations of the broader market level index, like the S&P 500 index, will offer a peek into volatility of the larger market. Similar results can be achieved by deducing the implied volatility from the option prices of the corresponding index.

Having a standard quantitative measure for volatility makes it easy to compare the possible price moves and the risk associated with different securities, sectors and markets.

The VIX Index is the first benchmark index introduced by the CBOE to measure the market’s expectation of future volatility. Being a forward looking index, it is constructed using the implied volatilities on S&P 500 index options (SPX) and represents the market’s expectation of 30-day future volatility of the S&P 500 index which is considered the leading indicator of the broad U.S. stock market. Introduced in 1993, the VIX Index is now an established and globally recognized gauge of U.S. equity market volatility. It is calculated in real-time based on the live prices of S&P 500 index. Calculations are performed and values are relayed during 2:15 a.m. CT and 8:15 a.m. CT, and between 8:30 a.m. CT and 3:15 p.m. CT. CBOE began dissemination of the VIX Index outside of U.S. trading hours in April 2016.