Exact Solution of Geometric Brownian Motion

In summary, when solving for the exact solution of the geometric brownian motion, using Ito's lemma and manipulating the expression obtained with dlogX_t is easier than directly using the expression dX_t / X_t = dlogX_t. This is because integrating both sides in the former format gives simpler integrals, while in the latter format, special care must be taken when integrating due to the presence of a Brownian motion measure.
  • #1
Tilde90
22
0
Hi!
Probably I am just confused, but why for the exact solution of the geometric brownian motion [itex]dX_t = \mu X_t dt+\sigma X_t dW_t[/itex] we have to apply Ito's lemma and manipulate the expression obtained with [itex]dlogX_t[/itex]? Couldn't we directly use the espression [itex]dX_t / X_t = dlogX_t[/itex] in the equation [itex]dX_t / X_t = \mu dt+\sigma dW_t[/itex]?
Thank you for your help!
 
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  • #2
Tilde90 said:
Hi!
Probably I am just confused, but why for the exact solution of the geometric brownian motion [itex]dX_t = \mu X_t dt+\sigma X_t dW_t[/itex] we have to apply Ito's lemma and manipulate the expression obtained with [itex]dlogX_t[/itex]? Couldn't we directly use the espression [itex]dX_t / X_t = dlogX_t[/itex] in the equation [itex]dX_t / X_t = \mu dt+\sigma dW_t[/itex]?
Thank you for your help!

Hey Tilde90 and welcome to the forums.

You could do this, but it's easier to have in the 1st format since you can integrate both sides which on the LHS gives you Xt - X0 and on the RHS gives you two integral expressions which you can solve using the Ito Lemma.

You also need to remember that in one integral you have a Brownian motion measure and because of this, you need to be careful in how you calculate the integral since it is very different to how you treat normal integrals like say f(x)dx across the real line.
 
  • #3
Thank you very much chiro.

I mistakenly believed that the "stochastic" integral were easier to calculate, as in the demonstration of the exact solution of the GBM with Ito's lemma it seems that they just integrate both terms of the SDE [itex]dlogX_t=(\mu-\frac{\sigma^2}{2})dt+\sigma dW_t[/itex].
 

1. What is Geometric Brownian Motion?

Geometric Brownian Motion (GBM) is a mathematical model used to describe the random movement of a variable over time. It is commonly used in finance to model the fluctuations of stock prices.

2. How is the exact solution of GBM calculated?

The exact solution of GBM can be calculated using the Black-Scholes equation, which takes into account the initial value of the variable, the drift rate, the volatility, and the time period. It is a closed-form solution and can be solved using mathematical techniques such as stochastic calculus.

3. What are the assumptions made in the exact solution of GBM?

The exact solution of GBM assumes that the variable follows a log-normal distribution, meaning that its natural logarithm is normally distributed. It also assumes a constant drift rate and volatility over time, and that the variable's movements are independent and random.

4. How is GBM used in real-world applications?

GBM is commonly used in finance to model stock prices, but it also has applications in other fields such as physics, biology, and engineering. It can be used to model the growth of populations, the diffusion of particles, and the movement of stock prices in different markets.

5. What are the limitations of the exact solution of GBM?

The exact solution of GBM has its limitations, as it is based on several assumptions that may not hold true in real-world scenarios. For example, it assumes a constant drift rate and volatility, which may not be the case in highly volatile markets. It also does not take into account external factors that may affect the variable's movement, such as news events or economic trends.

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