What is a Stochastic Differential Equation?

Consider the population growth model
\begin{equation}\label{eq:popgrowth}\frac{dN}{dt}=a(t)N(t),\ N(0)=N_0\end{equation}
where $N(t)$ is the size of a population at time $t$ and $a(t)$ is the relativive growth rate at time $t$. If $a(t)$ is completely known, one can easily solve \eqref{eq:popgrowth}. In fact, the solution would be $N(t)=N_0\exp\left(\int_0^t a(t)dt\right)$. Now suppose that $a(t)$ is not completely known but it can be written as $a(t)=r(t)+\mbox{noise}$. We do not know the exact behavior of noise but only its probability distribution. Such a case equations like \eqref{eq:popgrowth} is called a stochastic differential equation. More genrally, a stochastic differential equation can be written as
\begin{equation}\label{eq:sd}\frac{dX}{dt}=b(X(t))+B(X(t))\xi(t)\ (t>0),\ X(0)=x_0,\end{equation}
where $b: \mathbb{R}^n\longrightarrow\mathbb{R}^n$ is a smooth vector field and $X: [0,\infty)\longrightarrow\mathbb{R}^n$, $B: \mathbb{R}^n\longrightarrow\mathbb{M}^{n\times m}$ and $\xi(t)$ is an $m$-dimensional white noise. If $m=n$, $x_0=0$, $b=0$ and $B=I$, then \eqref{eq:sd} turns into
\begin{equation}\label{eq:wiener}\frac{dX}{dt}=\xi(t),\ X(0)=0\end{equation}
The solution of \eqref{eq:wiener} is denoted by $W(t)$ and is called the $n$-dimensional Wiener process or Brownian motion. In other words, white noise $\xi(t)$ is the time derivative of the Wiener process. Replace $\xi(t)$ in \eqref{eq:sd} by $\frac{W(t)}{dt}$ and divide the resulting equation by $dt$. Then we obtain
\begin{equation}\label{eq:sd2}dX(t)=b(X(t))dt+B(X(t))dW(t),\ X(0)=x_0\end{equation}
The stochastic differential equation \eqref{eq:sd2} is solved symbolically as
\begin{equation}\label{eq:sdsol}X(t)=x_0+\int_0^tb(X(s))ds+\int_0^tb(X(s))dW(s)\end{equation}
for all $t>0$. In order to make sense of $X(t)$ in \eqref{eq:sdsol} we will have to know what $W(t)$ is and what the integral $\int_0^tb(X(s))dW(s)$, which is called a stochastic integral, means.

References:

  1. Lawrence C. Evans, An Introduction to Stochastic Differential Equations, Lecture Notes
  2. Bernt Øksendal, Stochastic Differential Equations, An Introduction with Applications, 5th Edition, Springer, 2000

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