Home » Stochastic Calculus » SDE Numerics
Category Archives: SDE Numerics
Getting your feet wet numerically
Simulate the following stochastic differential equations:
- \[ dX(t) = – \lambda X(t) dt + dW(t) \]
- \[ dY(t) = – \lambda Y(t) dt +Y(t) dW(t) \]
by using the following Euler type numerical approximation
- \[X_{n+1} = X_n – \lambda X_n h + \sqrt{h} \eta_n\]
- \[Y_{n+1} = Y_n – \lambda Y_n h + \sqrt{h} Y_n\eta_n\]
where \(n=0,1,2,\dots\) and \(h >0\) is a small number that give the numerical step side. That is to say that we consider \( X_n \) as an approximation of \(X( t) \) and \( Y_n \) as an approximation of \(Y( t) \) each with \(t=h n\). Here \(\eta_n\) are a collection of mutually independent random variables each with a Gaussian distribution with mean zero and variance one. (That is \( N(0,1) \).)
Write code to simulate the two equations using the numerically methods suggested. Plot some trajectories. Describe how the behavior changes for different choices of \(\lambda\). Can you conjecture where it changes ? Compare and contrast the behavior of the two equations.
Tell your story with pictures.
Numerical SDEs – Euler–Maruyama method
If we wanted to simulate the SDE
\[dX_t = b(X_t) dt + \sigma(X_t) dW_t\]
on a computer then we truly want to approximate the associated integral equation over a sort time time interval of length \(h\). Namely,
\[X_{t+h} – X_t= \int_t^{t+h}b(X_s) ds + \int_t^{t+h}\sigma(X_s) dW_s\]
It is reasonable for \(s \in [t,t+h]\) to use the approximation
\begin{align}b(X_s) \approx b(X_t) \qquad\text{and}\qquad\sigma(X_s)\approx \sigma(X_t)\end{align}
which implies
\[X_{t+h} – X_t\approx b(X_t) h + \sigma(X_t) \big( W_{t+h}-W_{t}\big)\]
Since \(W_{t+h}-W_{t}\) is a Gaussian random variable with mean zero and variance \(h\), this discussion suggests the following numerical scheme:
\[ X_{n+1} = X_n + b(X_n) h + \sigma(X_n) \sqrt{h} \eta_n\]
where \(h\) is the time step and the \(\{ \eta_n : n=0,\dots\}\) are a collection of mutually independent standard Gaussian random variable (i.e. mean zero and variance 1). This is called the Euler–Maruyama method.
Use this method to numerically approximate (and plot) several trajectories of the following SDEs numerically.
- \[dX_t = -X_t dt + dW_t\]
- For \(r=-1, 0, 1/4, 1/2, 1\)
\[ dX_t = r X_t dt + X_t dW_t\]Does it head to infinity or approach zero ?Look at different small \(h\). Does the solution go negative ? Should it ? - \[dX_t = X dt -X_t^3 dt + \alpha dW_t\]
Try different values of \(\alpha\). For example, \(\alpha = 1/10, 1, 2 ,10\). How does it act ? What is the long time behavior ? How does it compare with what is learned in the “One dimensional stationary measure” problem about the stationary measure of this equation.
