Deriving Black–Scholes Call Delta and Comparing It with ITM Probability
Summary
The note derives the Black–Scholes call delta by differentiating the call-price formula with respect to the current stock price. Applying the chain rule produces terms involving the normal density and the sensitivities of the two standardized variables. An identity relating those variables cancels the density terms, leaving call delta as the standard normal cumulative probability evaluated at d1.
It then distinguishes delta from the risk-neutral probability that the option expires in the money. Under the lognormal stock-price model, that probability is the cumulative normal probability at d2, while delta uses d1. The two can be close when volatility multiplied by the square root of time is small, but they are not identical in general. The note assumes the Black–Scholes framework and gives no empirical evidence for how accurate this approximation is in particular markets or for specific options.
Key ideas
- Differentiating the Black–Scholes call price with respect to spot gives call delta.
- The chain-rule terms involving the normal density cancel through a relationship between d1 and d2.
- Call delta equals the standard normal cumulative distribution evaluated at d1.
- The risk-neutral probability of expiring in the money is the cumulative distribution at d2.
- Delta only approximates that probability when volatility and time to expiry make d1 and d2 close.
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# Derivation of Call Delta from Black Scholes Model
# Derivation of Call Delta from Black Scholes Model
How is call delta mathematically derived from Black Scholes Model (without approximation) ? Please help me understand each step mathematically. And how it is approximated to say that delta is the probability of option expiring in the money?
## Answer by Najee (score 7)
https://quant.stackexchange.com/a/71145
Here's a mathematical derivation of the Black-Scholes delta.
The call option price under the BS model is $$ C = S_0 N(d_1) - e^{-rT} K N(d_2) \quad\text{with}\quad d_{1,2} = \frac{\log(S_0\,e^{rT}/K)}{\sigma\sqrt{T}} \pm \frac12 \sigma\sqrt{T}, $$ where $N(x)$ is the CDF of standard normal.
Using the properties, $$ \frac{\partial d_1}{\partial S_0} = \frac{\partial d_2}{\partial S_0} = \frac{1}{S_0\sigma\sqrt{T}} $$ and \begin{gather*} d_1^2 - d_2^2 = (A+B)^2 - (A-B)^2 = 4AB = 2\log(S_0\,e^{rT}/K)\\ \quad\text{where}\quad A = \frac{\log(S_0\,e^{rT}/K)}{\sigma\sqrt{T}} \quad\text{and}\quad B = \frac{\sigma\sqrt{T}}{2}, \end{gather*} we differentiate $C$ with resect to the spot price $S_0$: \begin{align*} D &= \frac{\partial C}{\partial S_0} = \frac{\partial}{\partial S_0}\left( S_0 N(d_1) - e^{-rT} K N(d_2) \right) \\ &= N(d_1) + S_0 n(d_1) \frac{\partial d_1}{\partial S_0} - e^{-rT} K n(d_2) \frac{\partial d_2}{\partial S_0} \\ &= N(d_1) + \frac{n(d_1)}{\sigma\sqrt{T}} \left( 1 - e^{(d_1^2-d_2^2)/2}\frac{K}{S_0e^{rT}} \right) \\ &= N(d_1) + \frac{n(d_1)}{\sigma\sqrt{T}} \left( 1 - \frac{S_0e^{rT}}{K}\cdot\frac{K}{S_0e^{rT}} \right) = N(d_1). \end{align*}
## Answer by Kevin (score 3)
https://quant.stackexchange.com/a/49081
Look here for a detailed derivation of the formula for $\Delta$ (be aware that this particular website uses $r_d$ to denote the risk-free rate and $r_f$ to denote the dividend yield). You can always ask for more specific help regarding a particular step in the derivation.
It is easy to see that $\mathbb{Q}[\{S_T\geq K\}]= \Phi(d_2)$. Just replace $S_T=S_0\exp\left(\left( r-q-\frac{1}{2}\sigma^2\right)T +\sigma\sqrt{T}Z\right)$ where $Z\sim N(0,1)$ and isolate $Z$ on the left-hand side. This is the risk-neutral probability of expiring ITM. Note that $\Delta=\Phi(d_1)=\Phi(d_2+\sigma\sqrt{T})\approx \Phi(d_2)$. This is since $\sigma\sqrt{T}$ is typically very small.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.