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What is a Stochastic Differential Equation? 📂Stochastic Differential Equations

What is a Stochastic Differential Equation?

Definition 1

dX(t)=f(t,X(t))dt+g(t,X(t))dWt,t[t0,T],T>0 d X(t) = f \left( t, X(t) \right) dt + g \left( t, X(t) \right) d W_{t} \qquad , t \in \left[ t_{0} , T \right], T > 0

Equations of the form above are called Stochastic Differential Equations, abbreviated as SDEs. Here, ff and gg are called the drift and diffusion coefficient functions, respectively. For the initial condition X0:=X(t0)X_{0} := X \left( t_{0} \right), the integral form is represented as follows.

X(t)=X0+t0tf(s,X(s))ds+t0tg(s,X(s))dWs X(t) = X_{0} + \int_{t_{0}}^{t} f \left( s, X (s) \right) ds + \int_{t_{0}}^{t} g \left( s, X (s) \right) d W_{s}

Explanation

dXt=f(t,Xt)dt+g(t,Xt)dWt d X_{t} = f \left( t, X_{t}\right) dt + g \left( t, X_{t} \right) d W_{t}

If this form doesn’t bother you, it’s either because you’ve studied Ito calculus very well or know very little about differential equations, one or the other. For someone familiar with differential equations but not with SDEs, naturally gdWtg d W_{t} would be an eyesore. Unlike ODEs, SDEs incorporate this stochastic process, adding uncertainty to the model. Considering this term as 00, that is, gdWt=0g d W_{t} = 0 as a nondeterministic system, we can see the following. dX(t)=f(t,X(t))dt+g(t,X(t))dWt=f(t,X(t))dt+0=f(t,X(t))dt \begin{align*} d X(t) =& f \left( t, X(t) \right) dt + g \left( t, X(t) \right) d W_{t} \\ =& f \left( t, X(t) \right) dt + 0 \\ =& f \left( t, X(t) \right) dt \end{align*} Dividing both sides by dtdt, we get dX(t)dt=f(t,X(t)) {{ d X (t) } \over { dt }} = f \left( t, X(t) \right) Therefore, we can see that we have reclaimed the look of a well-known non-autonomous system.

Drift

In this explanation, recalling drift in time series analysis makes it quite natural to refer to the coefficient function ff as drift. This is because, regardless of the latter term, fdtf dt is the driving force that allows the system to be managed as a system itself.

Diffusion

Then, referring to gg as diffusion naturally suggests its role or characteristic of spreading or dispersing. In stochastic differential equations, this points to the concept of white noise, and unique results like Ito’s lemma arise due to such noise.


  1. Panik. (2017). Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling: p133. ↩︎