In finance, **volatility** (usually denoted by *σ*) is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns.

- Volatility terminology
- Mathematical definition
- Volatility origin
- Volatility for investors
- Volatility versus direction
- Volatility over time
- Alternative measures of volatility
- Implied volatility parametrisation
- Crude volatility estimation
- Estimate of compound annual growth rate (CAGR)
- Criticisms of volatility forecasting models
- See also
- References
- External links
- Further reading

Historic volatility measures a time series of past market prices. Implied volatility looks forward in time, being derived from the market price of a market-traded derivative (in particular, an option).

Volatility as described here refers to the **actual volatility**, more specifically:

**actual current volatility**of a financial instrument for a specified period (for example 30 days or 90 days), based on historical prices over the specified period with the last observation the most recent price.**actual historical volatility**which refers to the volatility of a financial instrument over a specified period but with the last observation on a date in the past- near synonymous is
**realized volatility**, the square root of the realized variance, in turn calculated using the sum of squared returns divided by the number of observations.

- near synonymous is
**actual future volatility**which refers to the volatility of a financial instrument over a specified period starting at the current time and ending at a future date (normally the expiry date of an option)

Now turning to implied volatility, we have:

**historical implied volatility**which refers to the implied volatility observed from historical prices of the financial instrument (normally options)**current implied volatility**which refers to the implied volatility observed from current prices of the financial instrument**future implied volatility**which refers to the implied volatility observed from future prices of the financial instrument

For a financial instrument whose price follows a Gaussian random walk, or Wiener process, the width of the distribution increases as time increases. This is because there is an increasing probability that the instrument's price will be farther away from the initial price as time increases. However, rather than increase linearly, the volatility increases with the square-root of time as time increases, because some fluctuations are expected to cancel each other out, so the most likely deviation after twice the time will not be twice the distance from zero.

Since observed price changes do not follow Gaussian distributions, others such as the Lévy distribution are often used.^{ [1] } These can capture attributes such as "fat tails". Volatility is a statistical measure of dispersion around the average of any random variable such as market parameters etc.

For any fund that evolves randomly with time, volatility is defined as the standard deviation of a sequence of random variables, each of which is the return of the fund over some corresponding sequence of (equally sized) times.

Thus, "annualized" volatility *σ*_{annually} is the standard deviation of an instrument's yearly logarithmic returns.^{ [2] }

The generalized volatility *σ*_{T} for time horizon *T* in years is expressed as:

Therefore, if the daily logarithmic returns of a stock have a standard deviation of *σ*_{daily} and the time period of returns is *P* in trading days, the annualized volatility is

A common assumption is that *P* = 252 trading days in any given year. Then, if *σ*_{daily} = 0.01, the annualized volatility is

The monthly volatility (i.e., *T* = 1/12 of a year or *P* = 252/12 = 21 trading days) would be

The formulas used above to convert returns or volatility measures from one time period to another assume a particular underlying model or process. These formulas are accurate extrapolations of a random walk, or Wiener process, whose steps have finite variance. However, more generally, for natural stochastic processes, the precise relationship between volatility measures for different time periods is more complicated. Some use the Lévy stability exponent *α* to extrapolate natural processes:

If *α* = 2 the Wiener process scaling relation is obtained, but some people believe *α* < 2 for financial activities such as stocks, indexes and so on. This was discovered by Benoît Mandelbrot, who looked at cotton prices and found that they followed a Lévy alpha-stable distribution with *α* = 1.7. (See New Scientist, 19 April 1997.)

Much research has been devoted to modeling and forecasting the volatility of financial returns, and yet few theoretical models explain how volatility comes to exist in the first place.

Roll (1984) shows that volatility is affected by market microstructure.^{ [3] } Glosten and Milgrom (1985) shows that at least one source of volatility can be explained by the liquidity provision process. When market makers infer the possibility of adverse selection, they adjust their trading ranges, which in turn increases the band of price oscillation.^{ [4] }

In September 2019, JPMorgan Chase determined the effect of US President Donald Trump's tweets, and called it the Volfefe index combining volatility and the covfefe meme.

Investors care about volatility for at least eight reasons:^{[ citation needed ]}

- The wider the swings in an investment's price, the harder emotionally it is to not worry;
- Price volatility of a trading instrument can define position sizing in a portfolio;
- When certain cash flows from selling a security are needed at a specific future date, higher volatility means a greater chance of a shortfall;
- Higher volatility of returns while saving for retirement results in a wider distribution of possible final portfolio values;
- Higher volatility of return when retired gives withdrawals a larger permanent impact on the portfolio's value;
- Price volatility presents opportunities to buy assets cheaply and sell when overpriced;
- Portfolio volatility has a negative impact on the compound annual growth rate (CAGR) of that portfolio
- Volatility affects pricing of options, being a parameter of the Black–Scholes model.

In today's markets, it is also possible to trade volatility directly, through the use of derivative securities such as options and variance swaps. See Volatility arbitrage.

Volatility does not measure the direction of price changes, merely their dispersion. This is because when calculating standard deviation (or variance), all differences are squared, so that negative and positive differences are combined into one quantity. Two instruments with different volatilities may have the same expected return, but the instrument with higher volatility will have larger swings in values over a given period of time.

For example, a lower volatility stock may have an expected (average) return of 7%, with annual volatility of 5%. This would indicate returns from approximately negative 3% to positive 17% most of the time (19 times out of 20, or 95% via a two standard deviation rule). A higher volatility stock, with the same expected return of 7% but with annual volatility of 20%, would indicate returns from approximately negative 33% to positive 47% most of the time (19 times out of 20, or 95%). These estimates assume a normal distribution; in reality stocks are found to be leptokurtotic.

Although the Black-Scholes equation assumes predictable constant volatility, this is not observed in real markets, and amongst the models are Emanuel Derman and Iraj Kani's^{ [5] } and Bruno Dupire's local volatility, Poisson process where volatility jumps to new levels with a predictable frequency, and the increasingly popular Heston model of stochastic volatility.^{ [6] }[link broken]

It is common knowledge that types of assets experience periods of high and low volatility. That is, during some periods, prices go up and down quickly, while during other times they barely move at all.^{ [7] } In foreign exchange market, price changes are seasonally heteroskedastic with periods of one day and one week.^{ [8] }^{ [9] }

Periods when prices fall quickly (a crash) are often followed by prices going down even more, or going up by an unusual amount. Also, a time when prices rise quickly (a possible bubble) may often be followed by prices going up even more, or going down by an unusual amount.

Most typically, extreme movements do not appear 'out of nowhere'; they are presaged by larger movements than usual. This is termed autoregressive conditional heteroskedasticity. Whether such large movements have the same direction, or the opposite, is more difficult to say. And an increase in volatility does not always presage a further increase—the volatility may simply go back down again.

Not only the volatility depends on the period when it is measured but also on the selected time resolution. The effect is observed due to the fact that the information flow between short-term and long-term traders is asymmetric. As a result, volatility measured with high resolution contains information that is not covered by low resolution volatility and vice versa.^{ [10] }

The risk parity weighted volatility of the three assets Gold, Treasury bonds and Nasdaq acting as proxy for the Marketportfolio seems to have a low point at 4% after turning upwards for the 8th time since 1974 at this reading in the summer of 2014.

Some authors point out that realized volatility and implied volatility are backward and forward looking measures, and do not reflect current volatility. To address that issue an alternative, ensemble measures of volatility were suggested. One of the measures is defined as the standard deviation of ensemble returns instead of time series of returns.^{ [11] } Another considers the regular sequence of directional-changes as the proxy for the instantaneous volatility.^{ [12] }

There exist several known parametrisations of the implied volatility surface, Schonbucher, SVI and gSVI.^{ [13] }

Using a simplification of the above formula it is possible to estimate annualized volatility based solely on approximate observations. Suppose you notice that a market price index, which has a current value near 10,000, has moved about 100 points a day, on average, for many days. This would constitute a 1% daily movement, up or down.

To annualize this, you can use the "rule of 16", that is, multiply by 16 to get 16% as the annual volatility. The rationale for this is that 16 is the square root of 256, which is approximately the number of trading days in a year (252). This also uses the fact that the standard deviation of the sum of *n* independent variables (with equal standard deviations) is √n times the standard deviation of the individual variables.

The average magnitude of the observations is merely an approximation of the standard deviation of the market index. Assuming that the market index daily changes are normally distributed with mean zero and standard deviation *σ*, the expected value of the magnitude of the observations is √(2/π)*σ* = 0.798*σ*. The net effect is that this crude approach underestimates the true volatility by about 20%.

Consider the Taylor series:

Taking only the first two terms one has:

Volatility thus mathematically represents a drag on the CAGR (formalized as the "volatility tax"). Realistically, most financial assets have negative skewness and leptokurtosis, so this formula tends to be over-optimistic. Some people use the formula:

for a rough estimate, where *k* is an empirical factor (typically five to ten).^{[ citation needed ]}

Despite the sophisticated composition of most volatility forecasting models, critics claim that their predictive power is similar to that of plain-vanilla measures, such as simple past volatility^{ [14] }^{ [15] } especially out-of-sample, where different data are used to estimate the models and to test them.^{ [16] } Other works have agreed, but claim critics failed to correctly implement the more complicated models.^{ [17] } Some practitioners and portfolio managers seem to completely ignore or dismiss volatility forecasting models. For example, Nassim Taleb famously titled one of his * Journal of Portfolio Management * papers "We Don't Quite Know What We are Talking About When We Talk About Volatility".^{ [18] } In a similar note, Emanuel Derman expressed his disillusion with the enormous supply of empirical models unsupported by theory.^{ [19] } He argues that, while "theories are attempts to uncover the hidden principles underpinning the world around us, as Albert Einstein did with his theory of relativity", we should remember that "models are metaphors – analogies that describe one thing relative to another".

The **Black–Scholes** or **Black–Scholes–Merton model** is a mathematical model for the dynamics of a financial market containing derivative investment instruments. From the parabolic partial differential equation in the model, known as the Black–Scholes equation, one can deduce the **Black–Scholes formula**, which gives a theoretical estimate of the price of European-style options and shows that the option has a *unique* price given the risk of the security and its expected return. The equation and model are named after economists Fischer Black and Myron Scholes; Robert C. Merton, who first wrote an academic paper on the subject, is sometimes also credited.

In mathematical finance, the **Greeks** are the quantities representing the sensitivity of the price of derivatives such as options to a change in underlying parameters on which the value of an instrument or portfolio of financial instruments is dependent. The name is used because the most common of these sensitivities are denoted by Greek letters. Collectively these have also been called the **risk sensitivities**, **risk measures** or **hedge parameters**.

In financial mathematics, the **implied volatility** (**IV**) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model, will return a theoretical value equal to the current market price of said option. A non-option financial instrument that has embedded optionality, such as an interest rate cap, can also have an implied volatility. Implied volatility, a forward-looking and subjective measure, differs from historical volatility because the latter is calculated from known past returns of a security. To understand where implied volatility stands in terms of the underlying, **implied volatility rank** is used to understand its implied volatility from a one-year high and low IV.

In finance, **moneyness** is the relative position of the current price of an underlying asset with respect to the strike price of a derivative, most commonly a call option or a put option. Moneyness is firstly a three-fold classification: if the derivative would have positive intrinsic value if it were to expire today, it is said to be **in the money**; if it would be worthless if expiring with the underlying at its current price it is said to be **out of the money**, and if the current underlying price and strike price are equal, it is said to be **at the money**. There are two slightly different definitions, according to whether one uses the current price (spot) or future price (forward), specified as "at the money spot" or "at the money forward", etc.

**Modern portfolio theory** (**MPT**), or **mean-variance analysis**, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of diversification in investing, the idea that owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. It uses the variance of asset prices as a proxy for risk.

In finance, the **Sharpe ratio** measures the performance of an investment such as a security or portfolio compared to a risk-free asset, after adjusting for its risk. It is defined as the difference between the returns of the investment and the risk-free return, divided by the standard deviation of the investment returns. It represents the additional amount of return that an investor receives per unit of increase in risk.

A **short-rate model**, in the context of interest rate derivatives, is a mathematical model that describes the future evolution of interest rates by describing the future evolution of the **short rate**, usually written .

In financial mathematics, the **Hull–White model** is a model of future interest rates. In its most generic formulation, it belongs to the class of no-arbitrage models that are able to fit today's term structure of interest rates. It is relatively straightforward to translate the mathematical description of the evolution of future interest rates onto a tree or lattice and so interest rate derivatives such as bermudan swaptions can be valued in the model.

In finance, **volatility arbitrage** is a term for financial arbitrage techniques directly dependent and based on volatility.

In mathematical finance, the **Black–Derman–Toy model** (**BDT**) is a popular short-rate model used in the pricing of bond options, swaptions and other interest rate derivatives; see Lattice model (finance) § Interest rate derivatives. It is a one-factor model; that is, a single stochastic factor—the short rate—determines the future evolution of all interest rates. It was the first model to combine the mean-reverting behaviour of the short rate with the log-normal distribution, and is still widely used.

In finance, the **Vasicek model** is a mathematical model describing the evolution of interest rates. It is a type of one-factor short-rate model as it describes interest rate movements as driven by only one source of market risk. The model can be used in the valuation of interest rate derivatives, and has also been adapted for credit markets. It was introduced in 1977 by Oldřich Vašíček, and can be also seen as a stochastic investment model.

**VIX** is the ticker symbol and the popular name for the Chicago Board Options Exchange's **CBOE Volatility Index**, a popular measure of the stock market's expectation of volatility based on S&P 500 index options. It is calculated and disseminated on a real-time basis by the CBOE, and is often referred to as the *fear index* or *fear gauge.*

In finance, a **volatility swap** is a forward contract on the future realised volatility of a given underlying asset. Volatility swaps allow investors to trade the volatility of an asset directly, much as they would trade a price index. Its payoff at expiration is equal to

A **local volatility** model, in mathematical finance and financial engineering, is an option pricing model that treats volatility as a function of both the current asset level and of time . As such, it is a generalisation of the Black–Scholes model, where the volatility is a constant.

In financial econometrics, the **Markov-switching multifractal (MSM)** is a model of asset returns developed by Laurent E. Calvet and Adlai J. Fisher that incorporates stochastic volatility components of heterogeneous durations. MSM captures the outliers, log-memory-like volatility persistence and power variation of financial returns. In currency and equity series, MSM compares favorably with standard volatility models such as GARCH(1,1) and FIGARCH both in- and out-of-sample. MSM is used by practitioners in the financial industry to forecast volatility, compute value-at-risk, and price derivatives.

**Forward volatility** is a measure of the implied volatility of a financial instrument over a period in the future, extracted from the term structure of volatility.

**Realized variance** or **realised variance** is the sum of squared returns. For instance the RV can be the sum of squared daily returns for a particular month, which would yield a measure of price variation over this month. More commonly, the realized variance is computed as the sum of squared intraday returns for a particular day.

The **Datar–Mathews Method** is a method for real options valuation. The method provides an easy way to determine the real option value of a project simply by using the average of positive outcomes for the project. The method can be understood as an extension of the net present value (NPV) multi-scenario Monte Carlo model with an adjustment for risk aversion and economic decision-making. The method uses information that arises naturally in a standard discounted cash flow (DCF), or NPV, project financial valuation. It was created in 2000 by Vinay Datar, professor at Seattle University; and Scott H. Mathews, Technical Fellow at The Boeing Company.

**Financial correlations** measure the relationship between the changes of two or more financial variables over time. For example, the prices of equity stocks and fixed interest bonds often move in opposite directions: when investors sell stocks, they often use the proceeds to buy bonds and vice versa. In this case, stock and bond prices are negatively correlated.

The **volatility tax** is a mathematical finance term, formalized by hedge fund manager Mark Spitznagel, describing the effect of large investment losses on compound returns. It has also been called **volatility drag, volatility decay** or **variance drain**. This is not literally a tax in the sense of a levy imposed by a government, but the mathematical difference between geometric averages compared to arithmetic averages. This difference resembles a tax due to the mathematics which impose a lower compound return when returns vary over time, compared to a simple sum of returns. This diminishment of returns is in increasing proportion to volatility, such that volatility itself appears to be the basis of a progressive tax. Conversely, fixed-return investments appear to be "volatility tax free".

- ↑ "Levy distribution".
*wilmottwiki.com*. - ↑ Calculating Historical Volatility: Step-by-Step Example at the Wayback Machine (archived 30 March 2012)
- ↑ Roll, R. (1984): "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market",
*Journal of Finance***39**(4), 1127–1139 - ↑ Glosten, L. R. and P. R. Milgrom (1985): "Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders",
*Journal of Financial Economics***14**(1), 71–100 - ↑ Derman, E., Iraj Kani (1994). ""Riding on a Smile." RISK, 7(2) Feb.1994, pp. 139–145, pp. 32–39" (PDF). Risk. Retrieved 1 June 2007.
`{{cite journal}}`

: Cite journal requires`|journal=`

(help)CS1 maint: multiple names: authors list (link) - ↑ "Volatility".
*wilmottwiki.com*. - ↑ "Taking Advantage Of Volatility Spikes With Credit Spreads".
- ↑ Müller, Ulrich A.; Dacorogna, Michel M.; Olsen, Richard B.; Pictet, Olivier V.; Schwarz, Matthias; Morgenegg, Claude (1 December 1990). "Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis".
*Journal of Banking & Finance*.**14**(6): 1189–1208. doi:10.1016/0378-4266(90)90009-Q. ISSN 0378-4266. - ↑ Petrov, Vladimir; Golub, Anton; Olsen, Richard (June 2019). "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time".
*Journal of Risk and Financial Management*.**12**(2): 54. doi: 10.3390/jrfm12020054 . - ↑ Muller, Ulrich A.; Dacorogna, Michel; Dave, Rakhal D.; Olsen, Richard; Pictet, Olivier V.; von Weizsäcker, Jakob (1997). "Volatilities of different time resolutions -- Analyzing the dynamics of market components".
*Journal of Empirical Finance*.**4**(2–3): 213–239. doi:10.1016/S0927-5398(97)00007-8. ISSN 0927-5398. - ↑ Sarkissian, Jack (2016). "Express Measurement of Market Volatility Using Ergodicity Concept". SSRN 2812353.
`{{cite journal}}`

: Cite journal requires`|journal=`

(help) - ↑ Petrov, Vladimir; Golub, Anton; Olsen, Richard (June 2019). "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time".
*Journal of Risk and Financial Management*.**12**(2): 54. doi: 10.3390/jrfm12020054 . - ↑ Babak Mahdavi Damghani & Andrew Kos (2013). "De-arbitraging with a weak smile". Wilmott.
`{{cite journal}}`

: Cite journal requires`|journal=`

(help) http://www.readcube.com/articles/10.1002/wilm.10201?locale=en - ↑ Cumby, R.; Figlewski, S.; Hasbrouck, J. (1993). "Forecasting Volatility and Correlations with EGARCH models".
*Journal of Derivatives*.**1**(2): 51–63. doi:10.3905/jod.1993.407877. S2CID 154028452. - ↑ Jorion, P. (1995). "Predicting Volatility in Foreign Exchange Market".
*Journal of Finance*.**50**(2): 507–528. doi:10.1111/j.1540-6261.1995.tb04793.x. JSTOR 2329417. - ↑ Brooks, Chris; Persand, Gita (2003). "Volatility forecasting for risk management".
*Journal of Forecasting*.**22**(1): 1–22. CiteSeerX 10.1.1.595.9113 . doi:10.1002/for.841. ISSN 1099-131X. - ↑ Andersen, Torben G.; Bollerslev, Tim (1998). "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts".
*International Economic Review*.**39**(4): 885–905. CiteSeerX 10.1.1.28.454 . doi:10.2307/2527343. JSTOR 2527343. - ↑ Goldstein, Daniel and Taleb, Nassim, (28 March 2007) "We Don't Quite Know What We are Talking About When We Talk About Volatility".
*Journal of Portfolio Management***33**(4), 2007. - ↑ Derman, Emanuel (2011): Models.Behaving.Badly: Why Confusing Illusion With Reality Can Lead to Disaster, on Wall Street and in Life”, Ed. Free Press.

- Graphical Comparison of Implied and Historical Volatility, video
- Diebold, Francis X.; Hickman, Andrew; Inoue, Atsushi & Schuermannm, Til (1996) "Converting 1-Day Volatility to h-Day Volatility: Scaling by sqrt(h) is Worse than You Think"
- A short introduction to alternative mathematical concepts of volatility
- Volatility estimation from predicted return density Example based on Google daily return distribution using standard density function
- Research paper including excerpt from report entitled Identifying Rich and Cheap Volatility Excerpt from Enhanced Call Overwriting, a report by Ryan Renicker and Devapriya Mallick at Lehman Brothers (2005).

- Bartram, Söhnke M.; Brown, Gregory W.; Stulz, Rene M. (August 2012). "Why Are U.S. Stocks More Volatile?" (PDF).
*Journal of Finance*.**67**(4): 1329–1370. doi:10.1111/j.1540-6261.2012.01749.x. S2CID 18587238. SSRN 2257549. - Natenberg, Sheldon (2015).
*Option Volatility and Pricing: Advanced Trading Strategies and Techniques*(Second ed.). New York. ISBN 978-0071818773.

This page is based on this Wikipedia article

Text is available under the CC BY-SA 4.0 license; additional terms may apply.

Images, videos and audio are available under their respective licenses.

Text is available under the CC BY-SA 4.0 license; additional terms may apply.

Images, videos and audio are available under their respective licenses.