Geometric Brownian motion Geometric Brownian motion A geometric Brownian motion (GBM) (also known as exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion (also called a Wiener process) with drift. It is an important example of stochastic processes satisfying a stochastic differential equation (SDE); in particular, it is used in mathematical finance to model stock prices in the Black–Scholes model. A stochastic process "S" is said to follow a GBM if it satisfies the following stochastic differential equation (SDE): where formula_2 is a Wiener process or Brownian motion, and formula_3 ('the
Reflected Brownian motion Reflected Brownian motion In probability theory, reflected Brownian motion (or regulated Brownian motion, both with the acronym RBM) is a Wiener process in a space with reflecting boundaries. RBMs have been shown to describe queueing models experiencing heavy traffic as first proposed by Kingman and proven by Iglehart and Whitt. A "d"–dimensional reflected Brownian motion "Z" is a stochastic process on formula_1 uniquely defined by where "X"("t") is an unconstrained Brownian motion and with "Y"("t") a "d"–dimensional vector where The reflection matrix describes boundary behaviour. In the interior of formula_4 the process behaves like a Wiener process, on the boundary