Deriving European Call Delta by Differentiating the Lognormal Density
Summary
The document clarifies a density differentiation step used to calculate the sensitivity of a discounted European call payoff to the initial stock price. Under the stated lognormal stock model, the terminal-price density depends on both the terminal value and the initial price. The key is therefore to write it as a two-argument function and differentiate with respect to the initial price while holding the terminal value fixed.
The accepted explanation identifies two problems in the attempted derivation: an incorrect expansion of the exponential and differentiation with respect to the terminal value instead of the initial price. Applying the chain rule to the density gives its derivative as the density multiplied by a score term involving the log price ratio, drift adjustment, initial price, volatility, and time. This supports the density-based expectation formula for delta stated in the question. The discussion is an algebraic clarification, not a comparison of pricing methods or empirical evidence, and it assumes the given lognormal model and parameters.
Key ideas
- The terminal-price density depends on the initial stock price as a parameter.
- For delta, differentiate the density with respect to the initial price while holding the terminal value fixed.
- The proposed exponential expansion in the question is incorrect.
- The density derivative equals the density times a score term obtained by the chain rule.
- The result is specific to the stated lognormal model and does not provide empirical evidence.
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# European call delta derivation
# European call delta derivation
Let's write $S(T) = S_T$ and $S(0) = S_0$. We want to compute $\frac{d}{dS_0}\mathbb{E}[f(S_T)]$. From a previous discussion this is equal to $$\mathbb{E}_{S_0}\left[f(S_T)\frac{g'_{S_0}(S_T)}{g_{S_0}(S_T)}\right]$$ where $f(S_T) = e^{-rT}(S_T - K)^{+}$. We need to find $g_{S_0}(S_T)$, the density of $S_T$ which is given by $$g(x) = \frac{1}{x\sigma\sqrt(T)}\phi\left(\frac{ln(x/S_0) - (r - \sigma^2/2)T}{\sigma\sqrt{T}}\right)$$ where $\phi$ is the standard normal density. In my notes it states that through algebra and calculus gives $$\frac{g'_{S_0}(x)}{g_{S_0}(x)} = \frac{ln(x/S_0) - (r - \sigma^2/2)T}{S_0 \sigma^2 T}$$
I am a bit confused but the mix of notation of $g(x)$ and $g_{S_0}(x)$, I want to show the detail of this to convince myself that this is true through "algebra and calculus". This is not an exercise for homework, I just do not understand the notation which is not allowing me to proceed. Any suggestions or comments are appreciated.
Attempted Derivation:
Through some algebra I was able to do expand $g(x)$:
\begin{align*} g(x) &= \frac{1}{x\sigma\sqrt(T)}\phi\left(\frac{ln(x/S_0) - (r - \sigma^2/2)T}{\sigma\sqrt{T}}\right)\\ &= \frac{1}{x\sigma\sqrt{T}}\cdot \frac{1}{\sqrt{2\pi}}\exp\left(-\left(\frac{ln(x/S_0) - (r-\sigma^2/2)T}{\sigma\sqrt{T}}\right)^2/2\right)\\ &= \frac{1}{x\sigma\sqrt{T}}\cdot \frac{1}{\sqrt{2\pi}}\exp\left(-\frac{r}{2\sigma^2} + \frac{ln(x/S_0)}{2\sigma^2 T} + \frac{1}{4}\right) \end{align*} Thus, $$g'(x) = \frac{ \exp\left(-\frac{r}{2\sigma^2} + \frac{ln(x/S_0)}{2\sigma^2 T} + \frac{1}{4}\right)}{2\sqrt{2\pi}\sigma^3 T^{3/2}x^2} - \frac{ \exp\left(-\frac{r}{2\sigma^2} + \frac{ln(x/S_0)}{2\sigma^2 T} + \frac{1}{4}\right)}{\sqrt{2\pi}\sigma\sqrt{T}x^2 } $$
As you can see this seems to be turning into an algebra nightmare. Unless I did something wrong I do not see how we will get $$\frac{g'_{S_0}(x)}{g_{S_0}(x)} = \frac{ln(x/S_0) - (r - \sigma^2/2)T}{S_0 \sigma^2 T}$$
## Answer by Gordon (score 3, accepted)
https://quant.stackexchange.com/a/33622
Two issues can be observed in your derivation. First, we note that \begin{align*} \exp\left(-\left(\frac{\ln(x/S_0) - (r-\sigma^2/2)T}{\sigma\sqrt{T}}\right)^2/2\right)\color{red}{\ne} \exp\left(-\frac{r}{2\sigma^2} + \frac{\ln(x/S_0)}{2\sigma^2 T} + \frac{1}{4}\right). \end{align*} Secondly, the derivative (i.e., the delta) is with respect to $S_0$ instead of $x$. In fact, for \begin{align*} g(S_0, x) &= \frac{1}{x\sigma\sqrt{T}}\phi\left(\frac{\ln(x/S_0) - (r - \sigma^2/2)T}{\sigma\sqrt{T}}\right)\\ &=\frac{1}{x\sigma\sqrt{T}}\cdot \frac{1}{\sqrt{2\pi}}\exp\left(-\left(\frac{\ln(x/S_0) - (r-\sigma^2/2)T}{\sigma\sqrt{T}}\right)^2/2\right), \end{align*} it can be easily verified that \begin{align*} \frac{\partial g(S_0, x)}{\partial S_0} &= g(S_0,x)\, \frac{\ln(x/S_0) - (r-\sigma^2/2)T}{S_0\sigma^2T}. \end{align*}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.