The Geometric Brownian Motion model is a continuous-time stochastic process in which a particle move according to a random fluctuations (Wiener process) and a drift term. The corresponding stochastic differential equation is:
\begin{equation} dX_{t}=\mu X_{t}dt+\sigma X_{t} dW_{t} \end{equation}
And the mean is given by: \begin{equation} E[X_{t}]=X_{0} e^{\mu t} \end{equation}
The Geometric Brownian Motion model recalls the Ornstein-Uhlbenck process, which stochastic differential equation is given by:
\begin{equation} dX_{t}=-\mu X_{t}dt+\sigma dW_{t} \end{equation}
And the mean is given by: \begin{equation} E[X_{t}]=X_{0} e^{-\mu t} \end{equation}
I have several questions concerning these two models:
- Could we say that the GBM model is the "reverse-in-time" of the OU process, given the change in sign in front of the drift term? If not, does exist a process equivalent to OU, but with a positive drift term?
- What are the consequences of having Xt in front of the σ parameter in the GBM model compared to a OU process?
- What are the covariance and variance expressions for a GBM model?
- Is the likelihood function the same as a general multivariate normal distribution?