Ito process

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 Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (\Omega, \mathcal{F}, \mathbb{F}, \mathbb{P})} be a probability space with a filtration Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbb{F}=(\mathcal{F}_t)_{t\geq 0}} that we consider as complete (that is to say, all sets which measure is equal to zero are contained in Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathcal{F}_0} ). Let also be Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle B=(B^1_t,\dots,B^d_t)_{t\geq 0}} a d-dimensional Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbb{F}} - Standard Brownian Motion.
Then we call Ito Process all process Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle (X_t)_{t\geq 0}} that can be written like :

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 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}

Where :

• Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle X_0} is ${\mathcal {F}}_{0}$ measurable
• $(K_{t})_{t\geq 0}$ is a progressively measurable process such as $\forall t\geq 0,\;\int _{0}^{t}|K_{s}|{\textrm {ds}}<+\infty$ almost surely.
• $(H_{t}^{i})_{t\geq 0,\ i\in [1\dots d]}$ is progressively measurable and such as $\forall i\in [1\dots d],\;\forall t\geq 0,\;\int _{0}^{t}(H_{s}^{i})^{2}\mathrm {ds} <+\infty$ almost surely.

We note then ${\mathcal {I}}$ the set of Ito Processes. We can also note that all Ito Processes are continuous and adapted to the filtration $\mathbb {F}$. We can also write the Ito Process under a 'differential form' :

$dX_{t}=K_{t}dt+\sum _{j=1}^{d}H_{t}^{j}dB_{t}^{j}$

Using the fact that the brownian part is a local martingal, and that all continuous local martingal with finite variations equal to zero in zero is indistinguishible of the null process, we can show that this decomposition is unique (except for indistinguishibility) for each Ito Process.

Stochastic Integral with respect to an Ito Process

Let $X$ be an Ito Process. We can define the set of processes ${\mathcal {L}}(X)$ that we can integer with respect to $X$ :

${\mathcal {L}}(X)=\{(Y_{t})_{t\geq 0}\mathrm {progressively} \;\mathrm {measurable} \;|\;\forall t\geq 0,\;\int _{0}^{t}|Y_{s}||K_{s}|{\textrm {ds}}+\sum _{j=1}^{d}\int _{0}^{t}(Y_{s}H_{s}^{j})^{2}{\textrm {dB}}_{s}^{j}<+\infty \;{\textrm {almost}}\;\mathrm {surely} \}$

We can then write :

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \int_0^tY_s\textrm{dX}_s = Y_0 + \int_0^t Y_sK_s\mathrm{ds} + \sum_{j=1}^d\int_0^t Y_sH^j_s\mathrm{dB}_s^j}

It is important to note that, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \forall X\in \mathcal{I},\ \mathcal{I}\in \mathcal{L}(X)} . Which means that any Ito Process can be integrated with respect to any other Ito Process. Moreover, the Stochastic Integral with respect to an Ito Process is still an Ito Process. This exceptional stability is one of the reasons of the wide use of Ito Processes. The other reason is the Ito Formula.