Pricing European Calls in a Lognormal Jump-Diffusion Model
Summary
The document derives a European call pricing representation for an asset whose returns combine continuous Brownian movement with independent Poisson jumps. When jump sizes are lognormally distributed, conditioning on the number of jumps makes the terminal log price normally distributed. Each possible jump count therefore corresponds to a Black–Scholes call with an adjusted volatility and rate.
The option value is expressed as a Poisson-weighted sum of those conditional Black–Scholes prices. The derivation uses the risk-neutral stock drift, including a compensator for the expected jump contribution, and then rearranges the weights to obtain an adjusted Poisson intensity. The rate adjustment reflects conditioning on the number and average size of jumps; it does not mean the market risk-free rate itself changes. The formula relies on the stated independent, identically distributed lognormal jump assumptions, and the infinite sum must be evaluated numerically in practice.
Key ideas
- Conditioning on the Poisson jump count produces a normal terminal log price under lognormal jump sizes.
- Each jump count yields a Black–Scholes price with count-specific volatility and rate parameters.
- The option value is a Poisson-weighted sum of the conditional prices.
- The compensator in the risk-neutral drift accounts for the expected contribution of jumps.
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Full text
# Black-Scholes formula for Poisson jumps
# Black-Scholes formula for Poisson jumps
For underlying asset $$d S = r S dt + \sigma S d W + (J-1)Sd N$$ here $W$ is a Brownian motion, $N(t)$ is Poisson process with intensity $\lambda.$
Suppose $J$ is log-normal with standard deviation $\sigma_J,$ denote $$k = E[J-1]$$ then the value of vanilla call is $$\sum\limits_{n=0}^{\infty}\dfrac{(\lambda'\tau)^n}{n!}e^{-\lambda'\tau}V_{BS}(S,t;\sigma_n,r_n)$$ Here $$\lambda' = \lambda(1+k),\ \tau =T - t$$ $$\sigma^2_n = \sigma^2 + \dfrac{n\sigma_J^2}{\tau},\ r_n=r-\lambda k+\dfrac{n\log(1+k)}{\tau}$$ and $V_{BS}$ is Black-Scholes value of a call without jumps.
The result seems to be the weighted mean of vanilla calls, but how to deduce this conclusion? Or is there any reference?
Suppose $\log J \sim N(\mu,\sigma^2_J)$ and $Z$ is standard normal, then we have \begin{eqnarray*} \log S_T &=& \log S_0 + (r - \dfrac{1}{2}\sigma^2)T + \sigma\sqrt{T} Z + N(T)\log(J)\\ &\sim& \log S_0 + \dfrac{1}{2}N(T)\sigma_J^2 +N(T)\mu + (r - \dfrac{1}{2}\hat{\sigma}^2)T + \hat{\sigma}\sqrt{T}Z\\ &=& \log \hat{S}_0 + (r - \dfrac{1}{2}\hat{\sigma}^2)T + \hat{\sigma}\sqrt{T} Z \end{eqnarray*} we omit the representations of $\hat{S}_0,\ \hat{\sigma}.$ So I think the price at $0$ should be $$\sum\limits_{n=0}^{\infty}\dfrac{(\lambda T)^n}{n!}e^{-\lambda T}V_{BS}(\hat{S}_0,0;\hat{\sigma}_n,r)$$
Why the author change the risk free rate $r_n$ and intensity $\lambda',$ does he change the measure for both Brownian motion and Poisson process?
## Answer by Gordon (score 7, accepted)
https://quant.stackexchange.com/a/34103
We assume that the process $\{J_t, \, t\ge 0\}$ is defined at the jump times of the Poisson process $\{N_t, \, t \ge 0\}$, and all the jump sizes are independent and identically distributed. That is, \begin{align*} Q_t \equiv \int_0^t (J_t-1) dN_t = \sum_{n=1}^{N_t} (J_i-1), \end{align*} where $J_i$, for $i=1, \ldots, \infty$, are independent and $\xi_i = \ln J_i \sim N\left(u, \sigma_J^2\right)$. Note that, the process \begin{align*} Q_t -\lambda k\, t \end{align*} is a martingale. Moreover, as the discount stock price process $\{e^{-rt}S_t, \ t\ge 0\}$ is a martingale, under the risk-neutral measure, we assume that the stock price process $\{S_t, \, t \ge 0\}$ satisfies an SDE of the form \begin{align*} dS_t = S_t \Big[(r-\lambda k)dt + \sigma dW_t + dQ_t\Big]. \end{align*} Then, \begin{align*} S_T &= e^{\left(r-\lambda k-\frac{1}{2}\sigma^2\right)T + \sigma W_T} \prod_{i=1}^{N_T} J_i = e^{\left(r-\lambda k-\frac{1}{2}\sigma^2\right)T + \sigma W_T + \sum_{i=1}^{N_T} \xi_i}. \end{align*} Consequently, $n\ge 0$, \begin{align*} S_T\, \mathbb{I}_{N_T=n} &=e^{\left(r-\lambda k + \frac{n u}{T}-\frac{1}{2}\sigma^2\right)T + \sigma W_T + \sum_{i=1}^n (\xi_i-u)}\\ &=e^{\left(r-\lambda k + \frac{n u}{T}-\frac{1}{2}\sigma^2\right)T + \sqrt{\sigma^2 + \frac{n \sigma_J^2}{T}}\sqrt{T} Z}\\ &=e^{\left(r_n -\frac{1}{2}\sigma_n^2\right)T + \sigma_n \sqrt{T} Z}, \end{align*} where $Z$ is a standard normal random variable, $\sigma_n= \sqrt{\sigma^2 + \frac{n \sigma_J^2}{T}}$, and \begin{align*} r_n &= r-\lambda k + \frac{n u}{T}-\frac{1}{2}\sigma^2 +\frac{1}{2}\sigma_n^2\\ &= r-\lambda k + \frac{n\ln(1+k)}{T}. \end{align*}
Therefore, \begin{align*} e^{-rT}E\left((S_T-K)^+ \right) &=\sum_{n=0}^{\infty}e^{-rT}E\left((S_T-K)^+ \mathbb{I}_{N_T=n}\right)P(N_T=n)\\ &=\sum_{n=0}^{\infty}e^{(-r+r_n)T}e^{-r_nT}E\left((S_T-K)^+ \mathbb{I}_{N_T=n}\right)P(N_T=n)\\ &=\sum_{n=0}^{\infty}\frac{(\lambda T)^n}{n!}e^{-\lambda T}e^{(-r+r_n)T}C(S_0, K, r_n, \sigma_n, T)\\ &=\sum_{n=0}^{\infty}\frac{(\lambda T)^n}{n!}e^{-\lambda T}e^{\left(-\lambda k + \frac{n\ln(1+k)}{T}\right)T}C(S_0, K, r_n, \sigma_n, T)\\ &=\sum_{n=0}^{\infty}\frac{(\lambda T)^n}{n!}e^{-\lambda(1+k) T}e^{\left(-\lambda k + \frac{n\ln(1+k)}{T}\right)T}C(S_0, K, r_n, \sigma_n, T)\\ &=\sum_{n=0}^{\infty}\frac{(\lambda' T)^n}{n!}e^{-\lambda' T}C(S_0, K, r_n, \sigma_n, T), \end{align*} where $\lambda'=(1+k)\lambda$ and $C(S_0, K, r_n, \sigma_n, T)$ is the Black-Scholes option price.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.