# Black–Scholes

(Redirected from Black Scholes)

The Black–Scholes [1] or Black–Scholes–Merton model is a mathematical model of a financial market containing certain derivative investment instruments. From the model, one can deduce the Black–Scholes formula, which gives a theoretical estimate of the price of European-style options. The formula led to a boom in options trading and legitimised scientifically the activities of the Chicago Board Options Exchange and other options markets around the world.[2] lt is widely used, although often with adjustments and corrections, by options market participants.[3]:751 Many empirical tests have shown that the Black–Scholes price is "fairly close" to the observed prices, although there are well-known discrepancies such as the "option smile".[3]:770–771

The model was first articulated by Fischer Black and Myron Scholes in their 1973 paper, "The Pricing of Options and Corporate Liabilities", published in the Journal of Political Economy. They derived a stochastic partial differential equation, now called the Black–Scholes equation, which governs the price of the option over time. The key idea behind the derivation was to hedge the option perfectly by buying and selling the underlying asset in just the right way, and consequently "eliminate risk". This hedge is called delta hedging and is the basis of more complicated hedging strategies such as those engaged in by Wall Street investment banks. The hedge implies that there is only one right price for the option and it is given by the Black–Scholes formula.

Robert C. Merton was the first to publish a paper expanding the mathematical understanding of the options pricing model, and coined the term Black–Scholes options pricing model. Merton and Scholes received the 1997 Nobel Prize in Economics (The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel) for their work. Though ineligible for the prize because of his death in 1995, Black was mentioned as a contributor by the Swedish academy.[4]

## Assumptions

The Black–Scholes model of the market for a particular stock makes the following explicit assumptions:

From these assumptions, Black and Scholes showed that “it is possible to create a hedged position, consisting of a long position in the stock and a short position in the option, whose value will not depend on the price of the stock.”[5]

Several of these assumptions of the original model have been removed in subsequent extensions of the model. Modern versions account for changing interest rates (Merton, 1976)[citation needed], transaction costs and taxes (Ingersoll, 1976)[citation needed], and dividend payout.[6]

## Notation

Let

$S$, be the price of the stock (please note inconsistencies as below).
$V(S, t)$, the price of a derivative as a function of time and stock price.
$C(S, t)$ the price of a European call option and $P(S, t)$ the price of a European put option.
$K$, the strike price of the option.
$r$, the annualized risk-free interest rate, continuously compounded (the force of interest).
$\mu$, the drift rate of $S$, annualized.
$\sigma$, the volatility of the stock's returns; this is the square root of the quadratic variation of the stock's log price process.
$t$, a time in years; we generally use: now=0, expiry=T.
$\Pi$, the value of a portfolio.

Finally we will use $N(x)$ which denotes the standard normal cumulative distribution function,

$N(x) = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{x} e^{-\frac{1}{2}z^2}\, dz$

$N'(x)$ which denotes the standard normal probability density function,

$N'(x) = \frac{1}{\sqrt{2\pi}} e^{-\frac{1}{2}x^2}$

### Inconsistencies

The reader is warned of the inconsistent notation that appears in this article. Thus the letter $S$ is used as:

1. a constant denoting the current price of the stock
2. a real variable denoting the price at an arbitrary time
3. a random variable denoting the price at maturity
4. a stochastic process denoting the price at an arbitrary time

It is also used in the meaning of (4) with a subscript denoting time, but here the subscript is merely a mnemonic.

In the partial derivatives, the letters in the numerators and denominators are, of course, real variables, and the partial derivatives themselves are, initially, real functions of real variables. But after the substitution of a stochastic process for one of the arguments they become stochastic processes.

The Black–Scholes PDE is, initially, a statement about the stochastic process $S$, but when $S$ is reinterpreted as a real variable, it becomes an ordinary PDE. It is only then that we can ask about its solution.

The parameter $u$ that appears in the discrete-dividend model and the elementary derivation is not the same as the parameter $\mu$ that appears elsewhere in the article. For the relationship between them see Geometric Brownian motion.

## The Black–Scholes equation

Simulated geometric Brownian motions with parameters from market data

As above, the Black–Scholes equation is a partial differential equation, which describes the price of the option over time. The equation is:

$\frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2} + rS\frac{\partial V}{\partial S} - rV = 0$

The key financial insight behind the equation is that one can perfectly hedge the option by buying and selling the underlying asset in just the right way and consequently “eliminate risk". This hedge, in turn, implies that there is only one right price for the option, as returned by the Black–Scholes formula (see the next section).

### Financial interpretation

The equation has a concrete interpretation that is often used by practitioners and is the basis for the common derivation given in the next subsection. The equation can be rewritten in the form:

$\frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2} = rV -rS\frac{\partial V}{\partial S}$

The left hand side consists of a "time decay" term, the change in derivative value due to to time increasing called theta, and a term involving the second spatial derivative gamma, the convexity of the derivative value with respect to the underlying value. The right hand side is the riskless return from a long position in the derivative and a short position consisting of $\frac{\partial V}{\partial S}$ shares of the underlying.

Black and Scholes' insight is that the portfolio represented by the right hand side is riskless: thus the equation says that the riskless return over any infinitesimal time interval, can be expressed as the sum of theta and a term incorporating gamma. For an option, theta is typically negative, reflecting the loss in value due to having less time for exercising the option (for a European call on an underlying without dividends, it is always negative). Gamma is typically positive and so the gamma term reflects the gains in holding the option. The equation states that over any infinitesimal time interval the loss from theta and the gain from the gamma term offset each other, so that the result is a return at the riskless rate.

From the viewpoint of the option issuer, e.g. an investment bank, the gamma term is the cost of hedging the option. (Since gamma is the greatest when the spot price of the underlying is near the strike price of the option, the seller's hedging costs are the greatest in that circumstance.)

### Derivation

The following derivation is given in Hull's Options, Futures, and Other Derivatives.[7]:287–288 That, in turn, is based on the classic argument in the original Black–Scholes paper.

Per the model assumptions above, the price of the underlying asset (typically a stock) follows a geometric Brownian motion. That is

$\frac{dS}{S} = \mu \,dt + \sigma \,dW\,$

where W is a stochastic variable (Brownian motion). Note that W, and consequently its infinitesimal increment dW, represents the only source of uncertainty in the price history of the stock. Intuitively, W(t) is a process that "wiggles up and down" in such a random way that its expected change over any time interval is 0. (In addition, its variance over time T is equal to T; see Wiener process: Basic properties); a good discrete analogue for W is a simple random walk. Thus the above equation states that the infinitesimal rate of return on the stock has an expected value of μ dt and a variance of $\sigma^2 dt$.

The payoff of an option $V(S,T)$ at maturity is known. To find its value at an earlier time we need to know how $V$ evolves as a function of $S$ and $t$. By Itō's lemma for two variables we have

$dV = \left(\mu S \frac{\partial V}{\partial S} + \frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2}\right)dt + \sigma S \frac{\partial V}{\partial S}\,dW$

Now consider a certain portfolio, called the delta-hedge portfolio, consisting of being short one option and long $\frac{\partial V}{\partial S}$ shares at time $t$. The value of these holdings is

$\Pi = -V + \frac{\partial V}{\partial S}S$

Over the time period $[t,t+\Delta t]$, the total profit or loss from changes in the values of the holdings is:

$\Delta \Pi = -\Delta V + \frac{\partial V}{\partial S}\,\Delta S$

Now discretize the equations for dS/S and dV by replacing differentials with deltas:

$\Delta S = \mu S \,\Delta t + \sigma S\,\Delta W\,$
$\Delta V = \left(\mu S \frac{\partial V}{\partial S} + \frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2}\right)\Delta t + \sigma S \frac{\partial V}{\partial S}\,\Delta W$

and appropriately substitute them into the expression for $\Delta \Pi$:

$\Delta \Pi = \left(-\frac{\partial V}{\partial t} - \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2}\right)\Delta t$

Notice that the $\Delta W$ term has vanished. Thus uncertainty has been eliminated and the portfolio is effectively riskless. The rate of return on this portfolio must be equal to the rate of return on any other riskless instrument; otherwise, there would be opportunities for arbitrage. Now assuming the risk-free rate of return is $r$ we must have over the time period $[t,t+\Delta t]$

$r\Pi\,\Delta t = \Delta \Pi$

If we now equate our two formulas for $\Delta\Pi$ we obtain:

$\left(-\frac{\partial V}{\partial t} - \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2}\right)\Delta t = r\left(-V + S\frac{\partial V}{\partial S}\right)\Delta t$

Simplifying, we arrive at the celebrated Black–Scholes partial differential equation:

$\frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2} + rS\frac{\partial V}{\partial S} - rV = 0$

With the assumptions of the Black–Scholes model, this second order partial differential equation holds for any type of option as long as its price function $V$ is twice differentiable with respect to $S$ and once with respect to $t$. Different pricing formulae for various options will arise from the choice of payoff function at expiry and appropriate boundary conditions.

Technical note: A subtlety obscured by the discretization approach above is that the infinitesimal change in the portfolio value was due to only the infinitesimal changes in the values of the assets being held, not changes in the positions in the assets. In other words, the porfolio was assumed to be self-financing. This can be proven in the continuous setting and uses basic results in the theory of stochastic differential equations.

## Black–Scholes formula

A European call valued using the Black-Scholes pricing equation for varying asset price S and time-to-expiry T. In this particular example, the strike price is set to unity.

The Black–Scholes formula calculates the price of European put and call options. This price is consistent with the Black–Scholes equation as above; this follows since the formula can be obtained by solving the equation for the corresponding terminal and boundary conditions.

The value of a call option for a non-dividend-paying underlying stock in terms of the Black–Scholes parameters is:

\begin{align} C(S, t) &= N(d_1)S - N(d_2) Ke^{-r(T - t)} \\ d_1 &= \frac{1}{\sigma\sqrt{T - t}}\left[\ln\left(\frac{S}{K}\right) + \left(r + \frac{\sigma^2}{2}\right)(T - t)\right] \\ d_2 &= \frac{1}{\sigma\sqrt{T - t}}\left[\ln\left(\frac{S}{K}\right) + \left(r - \frac{\sigma^2}{2}\right)(T - t)\right] \\ &= d_1 - \sigma\sqrt{T - t} \end{align}

The price of a corresponding put option based on put-call parity is:

\begin{align} P(S, t) &= Ke^{-r(T - t)} - S + C(S, t) \\ &= N(-d_2) Ke^{-r(T - t)} - N(-d_1) S \end{align}\,

For both, as above:

### Alternative formulation

Introducing some auxiliary variables allows the formula to be simplified and reformulated in a form that is often more convenient (this is a special case of the Black '76 formula):

\begin{align} C(F, \tau) &= D \left( N(d_+) F - N(d_-) K \right) \\ d_\pm &= \frac{1}{\sigma\sqrt{\tau}}\left[\ln\left(\frac{F}{K}\right) \pm \frac{1}{2}\sigma^2\tau\right] \\ d_\pm &= d_\mp \pm \sigma\sqrt{\tau} \end{align}

The auxiliary variables are:

• $\tau = T - t$ is the time to expiry (remaining time, backwards time)
• $D = e^{-r\tau}$ is the discount factor
• $F = e^{r\tau} S = \frac{S}{D}$ is the forward price of the underlying asset, and $S = DF$

with d+ = d1 and d = d2 to clarify notation.

Given put-call parity, which is expressed in these terms as:

$C - P = D(F - K) = S - D K$

the price of a put option is:

$P(F, \tau) = D \left[ N(-d_-) K - N(-d_+) F \right]$

### Interpretation

The Black–Scholes formula can be interpreted fairly easily, with the main subtlety the interpretation of the $N(d_\pm)$ (and a fortiori $d_\pm$) terms, particularly $d_+$ and why there are two different terms.[8]

The formula can be interpreted by first decomposing a call option into the difference of two binary options: an asset-or-nothing call minus a cash-or-nothing call (long an asset-or-nothing call, short a cash-or-nothing call). A call option exchanges cash for an asset at expiry, while an asset-or-nothing call just yields the asset (with no cash in exchange) and a cash-or-nothing call just yields cash (with no asset in exchange). The Black–Scholes formula is a difference of two terms, and these two terms equal the value of the binary call options. These binary options are much less frequently traded than vanilla call options, but are easier to analyze.

Thus the formula:

$C = D \left[ N(d_+) F - N(d_-) K \right]$

breaks up as:

$C = D N(d_+) F - D N(d_-) K$

where $D N(d_+) F$ is the present value of an asset-or-nothing call and $D N(d_-) K$ is the present value of a cash-or-nothing call. The D factor is for discounting, because the expiration date is in future, and removing it changes present value to future value (value at expiry). Thus $N(d_+) ~ F$ is the future value of an asset-or-nothing call and $N(d_-) ~ K$ is the future value of a cash-or-nothing call. In risk-neutral terms, these are the expected value of the asset and the expected value of the cash in the risk-neutral measure.

The naive, and not quite correct, interpretation of these terms is that $N(d_+) F$ is the probability of the option expiring in the money $N(d_+)$, times the value of the underlying at expiry F, while $N(d_-) K$ is the probability of the option expiring in the money $N(d_-),$ times the value of the cash at expiry K. This is obviously incorrect, as either both binaries expire in the money or both expire out of the money (either cash is exchanged for asset or it is not), but the probabilities $N(d_+)$ and $N(d_-)$ are not equal. In fact, $d_\pm$ can be interpreted as measures of moneyness (in standard deviations) and $N(d_\pm)$ as probabilities of expiring ITM (percent moneyness), in the respective numéraire, as discussed below. Simply put, the interpretation of the cash option, $N(d_-) K$, is correct, as the value of the cash is independent of movements of the underlying, and thus can be interpreted as a simple product of "probability times value", while the $N(d_+) F$ is more complicated, as the probability of expiring in the money and the value of the asset at expiry are not independent.[8] More precisely, the value of the asset at expiry is variable in terms of cash, but is constant in terms of the asset itself (a fixed quantity of the asset), and thus these quantities are independent if one changes numéraire to the asset rather than cash.

If one uses spot S instead of forward F, in $d_\pm$ instead of the $\frac{1}{2}\sigma^2$ term there is $\left(r \pm \frac{1}{2}\sigma^2\right)\tau,$ which can be interpreted as a drift factor (in the risk-neutral measure for appropriate numéraire). The use of d for moneyness rather than the standardized moneyness $m = \frac{1}{\sigma\sqrt{\tau}}\ln\left(\frac{F}{K}\right)$ – in other words, the reason for the $\frac{1}{2}\sigma^2$ factor – is due to the difference between the median and mean of the log-normal distribution; it is the same factor as in Itō's lemma applied to geometric Brownian motion. In addition, another way to see that the naive interpretation is incorrect is that replacing N(d+) by N(d) in the formula yields a negative value for out-of-the-money call options.[8]:6

In detail, the terms $N(d_1), N(d_2)$ are the probabilities of the option expiring in-the-money under the equivalent exponential martingale probability measure (numéraire=stock) and the equivalent martingale probability measure (numéraire=risk free asset), respectively.[8] The risk neutral probability density for the stock price $S_T \in (0, \infty)$ is

$p(S, T) = \frac{N^\prime [d_2(S_T)]}{S_T \sigma\sqrt{T}}$

where $d_2 = d_2(K)$ is defined as above.

Specifically, $N(d_2)$ is the probability that the call will be exercised provided one assumes that the asset drift is the risk-free rate. $N(d_1)$, however, does not lend itself to a simple probability interpretation. $SN(d_1)$ is correctly interpreted as the present value, using the risk-free interest rate, of the expected asset price at expiration, given that the asset price at expiration is above the exercise price.[9] For related discussion – and graphical representation – see section "Interpretation" under Datar–Mathews method for real option valuation.

The equivalent martingale probability measure is also called the risk-neutral probability measure. Note that both of these are probabilities in a measure theoretic sense, and neither of these is the true probability of expiring in-the-money under the real probability measure. To calculate the probability under the real ("physical") probability measure, additional information is required—the drift term in the physical measure, or equivalently, the market price of risk.

### Derivation

We now show how to get from the general Black–Scholes PDE to a specific valuation for an option. Consider as an example the Black–Scholes price of a call option, for which the PDE above has boundary conditions

\begin{align} C(0, t) &= 0\text{ for all }t \\ C(S, t) &\rightarrow S\text{ as }S \rightarrow \infty \\ C(S, T) &= \max\{S - K, 0\} \end{align}

The last condition gives the value of the option at the time that the option matures. The solution of the PDE gives the value of the option at any earlier time, $\mathbb{E}\left[\max\{S-K,0\}\right]$. To solve the PDE we recognize that it is a Cauchy–Euler equation which can be transformed into a diffusion equation by introducing the change-of-variable transformation

\begin{align} \tau &= T - t \\ u &= Ce^{r\tau} \\ x &= \ln\left(\frac{S}{K}\right) + \left(r - \frac{1}{2}\sigma^2\right)\tau \end{align}

Then the Black–Scholes PDE becomes a diffusion equation

$\frac{\partial u}{\partial\tau} = \frac{1}{2}\sigma^{2}\frac{\partial^2 u}{\partial x^2}$

The terminal condition $C(S, T) = \max\{S - K, 0\}$ now becomes an initial condition

$u(x, 0) = u_0(x) \equiv K(e^{\max\{x, 0\}} - 1)$

Using the standard method for solving a diffusion equation we have

$u(x, \tau) = \frac{1}{\sigma\sqrt{2\pi\tau}}\int_{-\infty}^{\infty}{u_0 [y]\exp{\left[-\frac{(x - y)^2}{2\sigma^2 \tau}\right]}}\,dy$

which, after some manipulations, yields

$u(x, \tau) = Ke^{x + \frac{1}{2}\sigma^2 \tau}N(d_1) - KN(d_2)$

where

\begin{align} d_1 &= \frac{1}{\sigma\sqrt{\tau}} \left[\left(x + \frac{1}{2} \sigma^{2}\tau\right) + \frac{1}{2} \sigma^2 \tau\right] \\ d_2 &= \frac{1}{\sigma\sqrt{\tau}} \left[\left(x + \frac{1}{2} \sigma^{2}\tau\right) - \frac{1}{2} \sigma^2 \tau\right] \end{align}

Reverting $u, x, \tau$ to the original set of variables yields the above stated solution to the Black–Scholes equation.

#### Other derivations

Above we used the method of arbitrage-free pricing ("delta-hedging") to derive the Black–Scholes PDE, and then solved the PDE to get the valuation formula. The Feynman-Kac formula says that the solution to this type of PDE, when discounted appropriately, is actually a martingale. Thus the option price is the expected value of the discounted payoff of the option. Computing the option price via this expectation is the risk neutrality approach and can be done without knowledge of PDEs.[8] Note the expectation of the option payoff is not done under the real world probability measure, but an artificial risk-neutral measure, which differs from the real world measure. For the underlying logic see section "risk neutral valuation" under Rational pricing as well as section "Derivatives pricing: the Q world" under Mathematical finance; for detail, once again, see Hull.[7]:307–309

## The Greeks

"The Greeks" measure the sensitivity of the value of a derivative or a portfolio to changes in parameter value(s) while holding the other parameters fixed. They are partial derivatives of the price with respect to the parameter values. One Greek, "gamma" (as well as others not listed here) is a partial derivative of another Greek, "delta" in this case.

The Greeks are important not only in the mathematical theory of finance, but also for those actively trading. Financial institutions will typically set limiting values for the Greeks that their traders must not exceed. Delta is the most important Greek and traders will zero their delta at the end of the day. Gamma and vega are also important but not as closely monitored.

The Greeks for Black–Scholes are given in closed form below. They can be obtained by differentiation of the Black–Scholes formula.[10]

Calls Puts
Delta $\frac{\partial C}{\partial S}$ $N(d_1)\,$ $-N(-d_1) = N(d_1) - 1\,$
Gamma $\frac{\partial^{2} C}{\partial S^{2}}$ $\frac{N'(d_1)}{S\sigma\sqrt{T - t}}\,$
Vega $\frac{\partial C}{\partial \sigma}$ $S N'(d_1) \sqrt{T-t}\,$
Theta $\frac{\partial C}{\partial t}$ $-\frac{S N'(d_1) \sigma}{2 \sqrt{T - t}} - rKe^{-r(T - t)}N(d_2)\,$ $-\frac{S N'(d_1) \sigma}{2 \sqrt{T - t}} + rKe^{-r(T - t)}N(-d_2)\,$
Rho $\frac{\partial C}{\partial r}$ $K(T - t)e^{-r(T - t)}N( d_2)\,$ $-K(T - t)e^{-r(T - t)}N(-d_2)\,$

Note that the gamma and vega formulas are the same for calls and puts. This can be seen directly from put–call parity, since the difference of a put and a call is a forward, which is linear in S and independent of σ (so the gamma and vega of a forward vanish).

In practice, some sensitivities are usually quoted in scaled-down terms, to match the scale of likely changes in the parameters. For example, rho is often reported multiplied by 10,000 (1 basis point rate change), vega by 100 (1 vol point change), and theta by 365 or 252 (1 day decay based on either calendar days or trading days per year).

(Vega is of course not a letter in the Greek alphabet; the name arises from reading the Greek letter ν (nu) as a V.)

## Extensions of the model

The above model can be extended for variable (but deterministic) rates and volatilities. The model may also be used to value European options on instruments paying dividends. In this case, closed-form solutions are available if the dividend is a known proportion of the stock price. American options and options on stocks paying a known cash dividend (in the short term, more realistic than a proportional dividend) are more difficult to value, and a choice of solution techniques is available (for example lattices and grids).

### Instruments paying continuous yield dividends

For options on indexes, it is reasonable to make the simplifying assumption that dividends are paid continuously, and that the dividend amount is proportional to the level of the index.

The dividend payment paid over the time period $[t, t + dt)$ is then modelled as

$qS_t\,dt$

for some constant $q$ (the dividend yield).

Under this formulation the arbitrage-free price implied by the Black–Scholes model can be shown to be

$C(S_0, t) = e^{-r(T - t)}[FN(d_1) - KN(d_2)]\,$

and

$P(S_0, t) = e^{-r(T - t)}[KN(-d_2) - FN(-d_1)]\,$

where now

$F = S_0 e^{(r - q)(T - t)}\,$

is the modified forward price that occurs in the terms $d_1, d_2$:

$d_1 = \frac{1}{\sigma\sqrt{T - t}}\left[\ln\left(\frac{F}{K}\right) + \frac{1}{2}\sigma^2(T - t)\right]$

and

$d_2 = d_1 - \sigma\sqrt{T - t}$

[11] Extending the Black Scholes formula Adjusting for payouts of the underlying.

### Instruments paying discrete proportional dividends

It is also possible to extend the Black–Scholes framework to options on instruments paying discrete proportional dividends. This is useful when the option is struck on a single stock.

A typical model is to assume that a proportion $\delta$ of the stock price is paid out at pre-determined times $t_1, t_2, \ldots$. The price of the stock is then modelled as

$S_t = S_0(1 - \delta)^{n(t)}e^{ut + \sigma W_t}$

where $n(t)$ is the number of dividends that have been paid by time $t$.

The price of a call option on such a stock is again

$C(S_0, T) = e^{-rT}[FN(d_1) - KN(d_2)]\,$

where now

$F = S_{0}(1 - \delta)^{n(T)}e^{rT}\,$

is the forward price for the dividend paying stock.

### American options

The problem of finding the price of an American option is related to the optimal stopping problem of finding the time to execute the option. Since the American option can be exercised at any time before the expiration date, the Black–Scholes equation becomes an inequality of the form

$\frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2} + rS\frac{\partial V}{\partial S} - rV \leq 0$[12]

With the terminal and (free) boundary conditions: $V(S, T) = H(S)$ and $V(S, t) \geq H(S)$ where $H(S)$ denotes the payoff at stock price $S$

In general this inequality does not have a closed form solution, though an American call with no dividends is equal to a European call and the Roll-Geske-Whaley method provides a solution for an American call with one dividend.[13][14]

Barone-Adesi and Whaley[15] is a further approximation formula. Here, the stochastic differential equation (which is valid for the value of any derivative) is split into two components: the European option value and the early exercise premium. With some assumptions, a quadratic equation that approximates the solution for the latter is then obtained. This solution involves finding the critical value, $s*$, such that one is indifferent between early exercise and holding to maturity.[16][17]

Bjerksund and Stensland[18] provide an approximation based on an exercise strategy corresponding to a trigger price. Here, if the underlying asset price is greater than or equal to the trigger price it is optimal to exercise, and the value must equal $S - X$, otherwise the option "boils down to: (i) a European up-and-out call option… and (ii) a rebate that is received at the knock-out date if the option is knocked out prior to the maturity date." The formula is readily modified for the valuation of a put option, using put call parity. This approximation is computationally inexpensive and the method is fast, with evidence indicating that the approximation may be more accurate in pricing long dated options than Barone-Adesi and Whaley.[19]

## Black–Scholes in practice

The normality assumption of the Black–Scholes model does not capture extreme movements such as stock market crashes.

The Black–Scholes model disagrees with reality in a number of ways, some significant. It is widely employed as a useful approximation, but proper application requires understanding its limitations – blindly following the model exposes the user to unexpected risk.

Among the most significant limitations are:

• the underestimation of extreme moves, yielding tail risk, which can be hedged with out-of-the-money options;
• the assumption of instant, cost-less trading, yielding liquidity risk, which is difficult to hedge;
• the assumption of a stationary process, yielding volatility risk, which can be hedged with volatility hedging;
• the assumption of continuous time and continuous trading, yielding gap risk, which can be hedged with Gamma hedging.

In short, while in the Black–Scholes model one can perfectly hedge options by simply Delta hedging, in practice there are many other sources of risk.

Results using the Black–Scholes model differ from real world prices because of simplifying assumptions of the model. One significant limitation is that in reality security prices do not follow a strict stationary log-normal process, nor is the risk-free interest actually known (and is not constant over time). The variance has been observed to be non-constant leading to models such as GARCH to model volatility changes. Pricing discrepancies between empirical and the Black–Scholes model have long been observed in options that are far out-of-the-money, corresponding to extreme price changes; such events would be very rare if returns were lognormally distributed, but are observed much more often in practice.

Nevertheless, Black–Scholes pricing is widely used in practice,[3]:751[20] for it is:

• easy to calculate
• a useful approximation, particularly when analyzing the direction in which prices move when crossing critical points
• a robust basis for more refined models
• reversible, as the model's original output, price, can be used as an input and one of the other variables solved for; the implied volatility calculated in this way is often used to quote option prices (that is, as a quoting convention)

The first point is self-evidently useful. The others can be further discussed:

Useful approximation: although volatility is not constant, results from the model are often helpful in setting up hedges in the correct proportions to minimize risk. Even when the results are not completely accurate, they serve as a first approximation to which adjustments can be made.

Basis for more refined models: The Black–Scholes model is robust in that it can be adjusted to deal with some of its failures. Rather than considering some parameters (such as volatility or interest rates) as constant, one considers them as variables, and thus added sources of risk. This is reflected in the Greeks (the change in option value for a change in these parameters, or equivalently the partial derivatives with respect to these variables), and hedging these Greeks mitigates the risk caused by the non-constant nature of these parameters. Other defects cannot be mitigated by modifying the model, however, notably tail risk and liquidity risk, and these are instead managed outside the model, chiefly by minimizing these risks and by stress testing.

Explicit modeling: this feature mean that, rather than assuming a volatility a priori and computing prices from it, one can use the model to solve for volatility, which gives the implied volatility of an option at given prices, durations and exercise prices. Solving for volatility over a given set of durations and strike prices one can construct an implied volatility surface. In this application of the Black–Scholes model, a coordinate transformation from the price domain to the volatility domain is obtained. Rather than quoting option prices in terms of dollars per unit (which are hard to compare across strikes and tenors), option prices can thus be quoted in terms of implied volatility, which leads to trading of volatility in option markets.

### The volatility smile

One of the attractive features of the Black–Scholes model is that the parameters in the model (other than the volatility) — the time to maturity, the strike, the risk-free interest rate, and the current underlying price – are unequivocally observable. All other things being equal, an option's theoretical value is a monotonic increasing function of implied volatility.

By computing the implied volatility for traded options with different strikes and maturities, the Black–Scholes model can be tested. If the Black–Scholes model held, then the implied volatility for a particular stock would be the same for all strikes and maturities. In practice, the volatility surface (the 3D graph of implied volatility against strike and maturity) is not flat.

The typical shape of the implied volatility curve for a given maturity depends on the underlying instrument. Equities tend to have skewed curves: compared to at-the-money, implied volatility is substantially higher for low strikes, and slightly lower for high strikes. Currencies tend to have more symmetrical curves, with implied volatility lowest at-the-money, and higher volatilities in both wings. Commodities often have the reverse behavior to equities, with higher implied volatility for higher strikes.

Despite the existence of the volatility smile (and the violation of all the other assumptions of the Black–Scholes model), the Black–Scholes PDE and Black–Scholes formula are still used extensively in practice. A typical approach is to regard the volatility surface as a fact about the market, and use an implied volatility from it in a Black–Scholes valuation model. This has been described as using "the wrong number in the wrong formula to get the right price."[21] This approach also gives usable values for the hedge ratios (the Greeks).

Even when more advanced models are used, traders prefer to think in terms of volatility as it allows them to evaluate and compare options of different maturities, strikes, and so on.

### Valuing bond options

Black–Scholes cannot be applied directly to bond securities because of pull-to-par. As the bond reaches its maturity date, all of the prices involved with the bond become known, thereby decreasing its volatility, and the simple Black–Scholes model does not reflect this process. A large number of extensions to Black–Scholes, beginning with the Black model, have been used to deal with this phenomenon.[22] See Bond option: Valuation.

### Interest-rate curve

In practice, interest rates are not constant – they vary by tenor, giving an interest rate curve which may be interpolated to pick an appropriate rate to use in the Black–Scholes formula. Another consideration is that interest rates vary over time. This volatility may make a significant contribution to the price, especially of long-dated options.This is simply like the interest rate and bond price relationship which is inversely related.

### Short stock rate

It is not free to take a short stock position. Similarly, it may be possible to lend out a long stock position for a small fee. In either case, this can be treated as a continuous dividend for the purposes of a Black–Scholes valuation, provided that there is no glaring asymmetry between the short stock borrowing cost and the long stock lending income.[citation needed]

## Criticism

Espen Gaarder Haug and Nassim Nicholas Taleb argue that the Black–Scholes model merely recast existing widely used models in terms of practically impossible "dynamic hedging" rather than "risk", to make them more compatible with mainstream neoclassical economic theory.[23] They also assert that Boness in 1964 had already published a formula that is "actually identical" to the Black–Scholes call option pricing equation.[24] Similar arguments were made in an earlier paper by Emanuel Derman and Nassim Taleb.[25] In response, Paul Wilmott has defended the model.[20][26]

British mathematician Ian Stewart published a criticism in which he suggested that "the equation itself wasn't the real problem" and he stated a possible role as "one ingredient in a rich stew of financial irresponsibility, political ineptitude, perverse incentives and lax regulation" due to its abuse in the financial industry.[27]

## Notes

1. ^ Although the original model assumed no dividends, trivial extensions to the model can accommodate a continuous dividend yield factor.

## References

1. ^ "Scholes". Retrieved March 26, 2012.
2. ^ MacKenzie, Donald (2006). An Engine, Not a Camera: How Financial Models Shape Markets. Cambridge, MA: MIT Press. ISBN 0-262-13460-8.
3. ^ a b c Bodie, Zvi; Alex Kane, Alan J. Marcus (2008). Investments (7th ed.). New York: McGraw-Hill/Irwin. ISBN 978-0-07-326967-2.
4. ^ "Nobel prize foundation, 1997 Press release". October 14, 1997. Retrieved March 26, 2012.
5. ^ Black, Fischer; Scholes, Myron. "The Pricing of Options and Corporate Liabilities". Journal of Political Economy 81 (3): 637–654.
6. ^ Merton, Robert. "Theory of Rational Option Pricing". Bell Journal of Economics and Management Science 4 (1): 141–183. doi:10.2307/3003143.
7. ^ a b Hull, John C. (2008). Options, Futures and Other Derivatives (7 ed.). Prentice Hall. ISBN 0-13-505283-1.
8. Nielsen, Lars Tyge (1993). "Understanding N(d1) and N(d2): Risk-Adjusted Probabilities in the Black-Scholes Model". Revue Finance (Journal of the French Finance Association) 14 (1): 95–106. Retrieved 2012 Dec 8, earlier circulated as INSEAD Working Paper 92/71/FIN (1992); abstract and link to article, published article.
9. ^ Don Chance (June 3, 2011). "Derivation and Interpretation of the Black–Scholes Model" (PDF). Retrieved March 27, 2012.
10. ^ Although with significant algebra; see, for example, Hong-Yi Chen, Cheng-Few Lee and Weikang Shih (2010). Derivations and Applications of Greek Letters: Review and Integration, Handbook of Quantitative Finance and Risk Management, III:491–503.
11. ^ http://finance.bi.no/~bernt/gcc_prog/recipes/recipes/node9.html
12. ^ André Jaun. "The Black-Scholes equation for American options". Retrieved May 5, 2012.
13. ^ Bernt Ødegaard (2003). "Extending the Black Scholes formula". Retrieved May 5, 2012.
14. ^ Don Chance (2008). "Closed-Form American Call Option Pricing: Roll-Geske-Whaley". Retrieved May 16, 2012.
15. ^ Giovanni Barone-Adesi and Robert E Whaley (June 1987). "Efficient analytic approximation of American option values". Journal of Finance. 42 (2): 301–20.
16. ^ Bernt Ødegaard (2003). "A quadratic approximation to American prices due to Barone-Adesi and Whaley". Retrieved June 25, 2012.
17. ^ Don Chance (2008). "Approximation Of American Option Values: Barone-Adesi-Whaley". Retrieved June 25, 2012.
18. ^ Petter Bjerksund and Gunnar Stensland, 2002. Closed Form Valuation of American Options
19. ^ American options
20. ^ a b
21. ^ Riccardo Rebonato (1999). Volatility and correlation in the pricing of equity, FX and interest-rate options. Wiley. ISBN 0-471-89998-4.
22. ^ Kalotay, Andrew (November 1995). "The Problem with Black, Scholes et al." (PDF). Derivatives Strategy.
23. ^ Espen Gaarder Haug and Nassim Nicholas Taleb (2011). Option Traders Use (very) Sophisticated Heuristics, Never the Black–Scholes–Merton Formula. Journal of Economic Behavior and Organization, Vol. 77, No. 2, 2011
24. ^ Boness, A James, 1964, Elements of a theory of stock-option value, Journal of Political Economy, 72, 163-175.
25. ^ Emanuel Derman and Nassim Taleb (2005). The illusions of dynamic replication, Quantitative Finance, Vol. 5, No. 4, August 2005, 323–326
26. ^ See also: Doriana Ruffinno and Jonathan Treussard (2006). Derman and Taleb’s The Illusions of Dynamic Replication: A Comment, WP2006-019, Boston University - Department of Economics.
27. ^ Ian Stewart (2012) The mathematical equation that caused the banks to crash, The Observer, February 12.

### Primary references

• Black, Fischer; Myron Scholes (1973). "The Pricing of Options and Corporate Liabilities". Journal of Political Economy 81 (3): 637–654. doi:10.1086/260062. [1] (Black and Scholes' original paper.)
• Merton, Robert C. (1973). "Theory of Rational Option Pricing". Bell Journal of Economics and Management Science (The RAND Corporation) 4 (1): 141–183. doi:10.2307/3003143. JSTOR 3003143. [2]
• Hull, John C. (1997). Options, Futures, and Other Derivatives. Prentice Hall. ISBN 0-13-601589-1.

### Historical and sociological aspects

• Bernstein, Peter (1992). Capital Ideas: The Improbable Origins of Modern Wall Street. The Free Press. ISBN 0-02-903012-9.
• MacKenzie, Donald (2003). "An Equation and its Worlds: Bricolage, Exemplars, Disunity and Performativity in Financial Economics". Social Studies of Science 33 (6): 831–868. doi:10.1177/0306312703336002. [3]
• MacKenzie, Donald; Yuval Millo (2003). "Constructing a Market, Performing Theory: The Historical Sociology of a Financial Derivatives Exchange". American Journal of Sociology 109 (1): 107–145. doi:10.1086/374404. [4]
• MacKenzie, Donald (2006). An Engine, not a Camera: How Financial Models Shape Markets. MIT Press. ISBN 0-262-13460-8.