Changing Poisson Intensities Under an Equivalent Martingale Measure
Summary
The discussion explains that changing probability measure can change the distribution of a counting process, including its Poisson intensity. An equivalent measure need not preserve the original Poisson law: one example tilts probabilities toward a particular count, resulting in a distribution that is no longer Poisson. A separate construction changes the intensity from its original value to a new one through a Radon–Nikodym density; under the new measure, the count minus its adjusted intensity times time is a martingale.
The finance example applies this idea to a stock whose jumps follow a compensated Poisson process. The discounted asset is not a martingale under the original measure when its drift differs from the interest rate. Choosing a new intensity to offset that drift gives a candidate equivalent martingale measure. This result is model-specific and requires the adjusted intensity to be admissible, including being positive; the discussion does not cover more general jump models or market completeness.
Key ideas
- Equivalent probability measures can change the probabilities assigned to Poisson counts.
- A general probability tilt may cause the resulting count distribution to stop being Poisson.
- Changing the Poisson intensity gives a new Poisson law under which the compensated count is a martingale.
- In the stated jump-stock model, adjusting intensity can make the discounted asset a martingale.
- The proposed intensity must satisfy admissibility conditions, including positivity.
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Full text
# Poisson process under equivalent martingale measure
# Poisson process under equivalent martingale measure
I have a stochastic process $N(t)$ which is equal to $n$ with probability
$P\{N(t) = n\}=\frac{\left(\lambda t \right)^{n}}{n!}e^{-\lambda t }$
where $t$ represents the time period. In other words, the corresponding process for a fixed $t$, is a random variable $N(t) \equiv N$ which is a (homogeneous) Poisson (point) process with the following Poisson distribution:
$P\{N = n\}=\frac{\lambda^{n}}{n!}e^{-\lambda}$
$P$ is the probability measure defined with respect to the sample space $\Omega$. Together with the sigma-algebra $A$, these three elements define my probability space.
Now I introduce the equivalent martingale measure (EMM) $Q$, which is equivalent to $P$ and has the property that under $Q$ each process becomes a martingale. To be more clear, this is the typical setup for the Black-Scholes type of pricing. For example, a stock which has a process under $P$ defined by
$dS_t=S_t\mu+S_t\sigma dW_t$
where $W_t$ is a Wiener process, has a drift given by the so called "risk-free rate" $r_f$ under $Q$ after also changing to the corresponding Wiener process under $Q$, i.e. $W^{Q}_t$
$dS_t=S_t r_f+S_t\sigma dW^{Q}_t$
The change of measure requires $W^{Q}_t=W_t+((\mu-r_f) / \sigma)t$
My question: is a Poisson process like the one presented above affected at all by the change of measure? My guess is that since it depends only on a certain parameter $\lambda$ and it is a counting process this is not the case, but I would like to hear further opinions. Any suggestion is well accepted and well taken
## Answer by Arshdeep (score 1)
https://quant.stackexchange.com/a/65407
Consider a radon nikodym derivative, the Random variable: $Z(w)= 1_{N(T,w)=1}+1-Pr(N(T)=1)$. It is admissible since it is always positive and has an expectation 1. This will lead us to the formation of an equivalent measure, which I will denote by $'$.
We start with the easy theorem that $E'(X)=E(XZ)$ for any random variable X, and RND $Z$
$E'(1_{N(T)=1})=E(1_{N(T)=1}*(1_{N(T)=1}+1-Pr(N(T)=1)))$
Thus,
$Pr'(N(T)=1)=2Pr(N(T)=1)-[Pr(N(T)=1)]^2 > Pr(N(T)=1)$
Note also
$Pr'(N(T)=2)=Pr(N(T)=2)[1-Pr(N(T)=1)] < Pr[N(T)=2]$
So the new measure is providing more probability mass at $1$, while taking it away from all the other points (like $2$, as shown above). This is no longer a poisson process, which you can verify with a bit of algebra.
Comment: I mistook your question as to finding a EMM for the poisson process, which was admittedly hard. But establishing that it is sensitive to a change of equivalent measure is not hard. Almost all non degenerate process are sensitive to a change of measure.
## Answer by ir7 (score 1)
https://quant.stackexchange.com/a/65418
$N_t$ process comes with its own Poisson law (probability measure) $P$ defined via intensity $\lambda$. Under it, $N_t-\lambda t$ is a martingale wrt ${\cal F}_t =\sigma(N_u | u\in [0,t])$ (as $E^P[N_t]=\lambda t$ and $N_t-\lambda t$ has independent increments).
Any other equivalent Poisson law, $Q$, defined via a given intensity $\gamma$, can be built using Radon-Nikodym density
$$ \frac{dQ}{dP}{\bigg|}_{{\cal F}_t} = \exp\left( \ln(\gamma/\lambda)N_t-(\gamma -\lambda)t \right), $$ by noticing that
$$ Q(N_t=n) = \exp\left( \ln(\gamma/\lambda)n-(\gamma -\lambda)t \right) P(N_t=n)$$ $$= \exp\left( \ln(\gamma/\lambda)n-(\gamma -\lambda)t \right) (n!)^{-1}(\lambda t)^n\exp(-\lambda t) = (n!)^{-1}(\gamma t)^n\exp(-\gamma t).$$
Under $Q$, $N_t-\gamma t$ is a ${\cal F}_t$-martingale.
In the finance context, if we only have a simple asset modeled by process (an independent Brownian motion can be added - see Merton's model, for example, but we will keep it about Poisson processes here):
$$ dS_t= S_{t^-} (\mu dt + \zeta d(N_t-\lambda t)),$$
under $P$, $\zeta>0$, then (with constant interest rate $r$)
$$ d(e^{-rt}S_t)/(e^{-rt}S_{t^-}) = (\mu -r) dt + \zeta d(N_t -\lambda t) $$
which shows that $e^{-rt}S_t$ is not a martingale when $\mu \not= r$.
The discounted asset is a martingale under Poisson $Q$ built as above for intensity:
$$\gamma = \lambda - \frac{\mu -r}{\zeta}, $$
making $Q$ an EMM.
(See notes suggested in the comments and also these ones.)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.