In this article, Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022) explains how implied volatility is computed from option market prices and a option pricing model.
Volatility is a measure of fluctuations observed in an asset’s returns over a period of time. The standard deviation of historical asset returns is one of the measures of volatility. In option pricing models like the Black-Scholes-Merton model, volatility corresponds to the volatility of the underlying asset’s return. It is a key component of the model because it is not directly observed in the market and cannot be directly computed. Moreover, volatility has a strong impact on the option value.
Mathematically, in a reverse way, implied volatility is the volatility of the underlying asset which gives the theoretical value of an option (as computed by Black-Scholes-Merton model) equal to the market price of that option.
Implied volatility is a forward-looking measure because it is a representation of expected price movements in an underlying asset in the future.
Computation methods for implied volatility
The Black-Scholes-Merton (BSM) model provides an analytical formula for the price of both a call option and a put option.
The value for a call option at time t is given by:
The value for a put option at time t is given by:
where the parameters d1 and d2 are given by:,
with the following notations:
St : Price of the underlying asset at time t
t: Current date
T: Expiry date of the option
K: Strike price of the option
r: Risk-free interest rate
σ: Volatility of the underlying asset
N(.): Cumulative distribution function for a normal (Gaussian) distribution. It is the probability that a random variable is less or equal to its input (i.e. d₁ and d₂) for a normal distribution. Thus, 0 ≤ N(.) ≤ 1
From the BSM model, both for a call option and a put option, the option price is an increasing function of the volatility of the underlying asset: an increase in volatility will cause an increase in the option price.
Figures 1 and 2 below illustrate the relationship between the value of a call option and a put option and the level of volatility of the underlying asset according to the BSM model.
Figure 1. Call option value as a function of volatility.
Source: computation by the author (BSM model)
Figure 2. Put option value as a function of volatility.
Source: computation by the author (BSM model)
You can download below the Excel file for the computation of the value of a call option and a put option for different levels of volatility of the underlying asset according to the BSM model.
We can observe that the call and put option values are a monotonically increasing function of the volatility of the underlying asset. Then, for a given level of volatility, there is a unique value for the call option and a unique value for the put option. This implies that this function can be reversed; for a given value for the call option, there is a unique level of volatility, and similarly, for a given value for the put option, there is a unique level of volatility.
The BSM formula can be reverse-engineered to compute the implied volatility i.e., if we have the market price of the option, the market price of the underlying asset, the market risk-free rate, and the characteristics of the option (the expiration date and strike price), we can obtain the implied volatility of the underlying asset by inverting the BSM formula.
Consider a call option with a strike price of 50 € and a time to maturity of 0.25 years. The market risk-free interest rate is 2% and the current price of the underlying asset is 50 €. Thus, the call option is ‘at-the-money’. If the market price of the call option is equal to 2 €, then the associated level of volatility (implied volatility) is equal to 18.83%.
You can download below the Excel file below to compute the implied volatility given the market price of a call option. The computation uses the Excel solver.
Volatility smile is the name given to the plot of implied volatility against different strikes for options with the same time to maturity. According to the BSM model, it is a horizontal straight line as the model assumes that the volatility is constant (it does not depend on the option strike). However, in practice, we do not observe a horizontal straight line. The curve may be in the shape of the alphabet ‘U’ or a ‘smile’ which is the usual term used to refer to the observed function of implied volatility.
Figure 3 below depicts the volatility smile for call options on the Apple stock on May 13, 2022.
Figure 3. Volatility smile for call options on Apple stock.
Source: Computation by author.
We can also observe that the for a specific time to maturity, the implied volatility is minimum when the option is at-the-money.
An essential assumption of the BSM model is that the returns of the underlying asset follow geometric Brownian motion (corresponding to log-normal distribution for the price at a given point in time) and the volatility of the underlying asset price remains constant over time until the expiration date. Thus theoretically, for a constant time to maturity, the plot of implied volatility and strike price would be a horizontal straight line corresponding to a constant value for volatility.
Volatility surface is obtained when values for implied volatilities are calculated for options with different strike prices and times to maturity.
CBOE Volatility Index
The Chicago Board Options Exchange publishes the renowned Volatility Index (also known as VIX) which is an index based on the implied volatility of 30-day option contracts on the S&P 500 index. It is also called the ‘fear gauge’ and it is a representation of the market outlook for volatility for the next 30 days.
Black F. and M. Scholes (1973) “The Pricing of Options and Corporate Liabilities” The Journal of Political Economy, 81, 637-654.
Dupire B. (1994). “Pricing with a Smile” Risk Magazine 7, 18-20.
Merton R.C. (1973) “Theory of Rational Option Pricing” Bell Journal of Economics, 4, 141–183.
Related posts on the SimTrade blog
▶ Gupta A. Options
▶ Walia J. Black-Scholes-Merton option pricing model
▶ Walia J. Brownian Motion in Asset Pricing
▶ Walia J. Standard deviation
About the author
The article was written in May 2022 by Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022).