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?
2 Answers
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*}
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.
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1$\begingroup$ That derivation is excellent but it starts with a small typo: in BSCallPrice the roles of $r_f$ the risk free rate and $r_d$ the dividend rate are reversed. $\endgroup$– Alex COct 6, 2019 at 15:39
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$\begingroup$ I think it's not a typo because they use $r_f$ constantly and (as far as I checked) consistently to denote the dividend yield and $r_d$ as risk-free interest rate. So I believe they do this intentionally. However, I do agree with you that this notation is odd because $r_f$ should be the risk-free rate (or perhaps the rate in a foreign currcency) but not the dividend yield. $\endgroup$– KevinOct 6, 2019 at 16:33
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$\begingroup$ But I edited my answer and highlighted that notation issue. Thank you for the hint, Alex! $\endgroup$– KevinOct 6, 2019 at 16:41
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1$\begingroup$ N represents the cumulative normal distribution, and n represents the normal density. Cumulative is integral of density, so density is then derivative of the cumulative - remember integral is anti-derivative, so derivative of anti--derivative is then the function. For technical approach, please see Leibniz rule (en.wikipedia.org/wiki/Leibniz_integral_rule). As @KeSchn outlined above, if we take derivative of N(s), we get n(s), and if we want to take derivative of N(d_1) where d_1 is a function of s, we will need to add chain rule: n(d1)*d(d1)/dS $\endgroup$ Oct 6, 2019 at 20:00
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1$\begingroup$ A reminder what N(.) and n(.) look like dwaincsql.files.wordpress.com/2015/05/normal-pdf-cdf-1.png $\endgroup$– Alex COct 6, 2019 at 22:42