Ito process: Difference between revisions
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Let <math>(\Omega, F, \mathbb{F}, \mathbb{P})</math> be a probability space with a filtration <math>\mathbb{F}=(F_t)_{t\geq 0}</math> that we consider as complete (that is to say, all sets which measure is null are contained in <math>F_0</math> | Let <math>(\Omega, F, \mathbb{F}, \mathbb{P})</math> be a probability space with a filtration <math>\mathbb{F}=(F_t)_{t\geq 0}</math> that we consider as complete (that is to say, all sets which measure is null are contained in <math>F_0</math> | ||
Let also be <math>B=(B^1_t,\dots,B^d_t)_{t\geq 0}</math> a ''d''-dimensional <math>\mathbb{F}</math>- Standard Brownian Motion. | Let also be <math>B=(B^1_t,\dots,B^d_t)_{t\geq 0}</math> a ''d''-dimensional <math>\mathbb{F}</math>- Standard Brownian Motion. | ||
Then we call Ito Process all process <math>(X_t)_{t\geq 0}</math> that can be written like : | Then we call Ito Process all process <math>(X_t)_{t\geq 0}</math> that can be written like : | ||
<math>X_t = X_0 + \int_0^t K_s\mathrm{ds} + \sum_{j=1}^d\int_0^t | |||
<math>X_t = X_0 + \int_0^t K_s\mathrm{ds} + \sum_{j=1}^d\int_0^t H^j_s*\mathrm{dB}_s^j</math> | |||
Where : | Where : | ||
* <math>X_0</math> is <math>F_0</math> measurable | * <math>X_0</math> is <math>F_0</math> measurable | ||
* <math>(K_t)_{t\geq 0}</math> is a progressively measurable process such as <math>\forall t\geq 0,\ \int_0^t|K_s|\textrm{ds}<+\infty</math> almost surely. | * <math>(K_t)_{t\geq 0}</math> is a progressively measurable process such as <math>\forall t\geq 0,\ \int_0^t|K_s|\textrm{ds}<+\infty</math> almost surely. | ||
* <math>(H^i_t)_{t\geq 0,\ i\in[1\dots d]}</math> is progressively measurable and such as <math>\forall i\in [1\dots d],\ \forall t\geq 0,\ \int_0^t(H_s^i)^2\mathrm(ds)<+\infty</math> almost surely. | |||
Revision as of 14:16, 28 December 2008
An Ito Process is a type of stochastic process described by Japanese mathematician Kiyoshi Ito, which can be written as the sum of the integral of a process over time and of another process over a Brownian Motion.
Those processes are the base of Stochastic Integration, and are therefore widely used in Financial Mathematics and Stochastic Calculus.
Description of the Ito Processes
Let be a probability space with a filtration that we consider as complete (that is to say, all sets which measure is null are contained in
Let also be a d-dimensional - Standard Brownian Motion.
Then we call Ito Process all process that can be written like :
Where :
- is measurable
- is a progressively measurable process such as almost surely.
- is progressively measurable and such as almost surely.