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Simulating Geometric Brownian Motion After a Change of Measure

Article Quant Q&A · Author: Elekko

Summary

This discussion explains how to simulate a stock price when a change of probability measure shifts the drift of a geometric Brownian motion. Under the new measure, the process has drift r plus volatility times the market-price-of-risk term, while its Brownian increments remain standard normal. For a constant shift, the usual exact lognormal time-step formula applies with the adjusted drift; the normal random draw does not need a shifted mean.

For a time-varying shift, the answer expresses the step using the integral of that shift over the interval, then gives a small-step approximation using its value at the start of the interval. That approximation assumes the shift is continuous. The exchange offers a conceptual clarification and a simulation rule, but does not present numerical experiments or discuss calibration of the shift, discretization error, or broader measure-change assumptions.

Key ideas

  • Under the changed measure, the drift becomes r plus volatility multiplied by the measure-change shift.
  • For a constant shift, simulate with the adjusted drift and standard normal innovations.
  • A time-varying shift enters the log-price step through its integral over the time interval.
  • Using the shift at the interval start is an approximation that relies on continuity.

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Full text
# Simulate drifted geometric brownian motion under new measure


# Simulate drifted geometric brownian motion under new measure












I have a very fundamental question regarding simulation of DRIFTED geometric brownian motion.

We have the standard Blackos Scholes model:

$dS(t)=r S(t)dt+\sigma S(t) dW^{\mathbb{P}}(t)$, where $W^{\mathbb{P}}(t)$ is the standard Wiener process under probability measure $\mathbb{P}$.

If we want to simulate this, using constant $\Delta t$, we use the recursive formula:

$S_{t+1}=S_te^{(r-\frac{\sigma^2}{2})\Delta t+\sigma \sqrt{\Delta t} Z_t }$, where $Z_t \sim N(0,1)$.

Now assume that we want to change the drift such that:

$W^{\mathbb{Q}}(t) = W^{\mathbb{P}}(t) - \int_0^t \theta_s ds$ is a brownian motion under $\mathbb{Q}$ such that:

$dS(t)=(r + \sigma \theta )S(t)dt+\sigma S(t) dW^{\mathbb{Q}}(t)$

Now this is where I become unsure. If I want to simulate the drifted process, is it just fine to use the similar method as:

$S_{t+1}=S_te^{(r+\sigma \theta-\frac{\sigma^2}{2})\Delta t+\sigma \sqrt{\Delta t} Z_t }$, where $Z_t \sim N(0,1)$.

OR is it that I have to use $Z_t \sim N(\theta, 1)$?

Im not that strong in the change of measure part, so thats why I'm a bit unsure. Would appreciate for help.

Thanks

## Answer by Mark Joshi (score 1)

https://quant.stackexchange.com/a/26323

in the new measure, the stock has drift $r + \sigma \theta$ so yes you just proceed with that drift as you say. If $\theta$ is time dependent, it gets more complicated.

## Answer by Gordon (score 1)

https://quant.stackexchange.com/a/26331

Note that, under measure $Q$, the dynamics is of the form \begin{align*} dS_t = S_t \big[(r+ \sigma \theta_t) dt + \sigma dW_t^Q \big]. \end{align*} Then, for $\Delta>0$ sufficiently small, \begin{align*} S_{t+\Delta} &= S_te^{\left(r-\frac{1}{2}\sigma^2\right)\Delta + \sigma \int_t^{t+\Delta} \theta_s ds + \sigma \left(W_{t+\Delta}^Q-W_t^Q\right)}\\ &\approx S_te^{\left(r-\frac{1}{2}\sigma^2 + \sigma \theta_t\right)\Delta + \sigma \sqrt{\Delta} Z}, \end{align*} assuming that $\theta_t$ is continuous, where $Z\sim N(0, 1)$.

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