Simulate multivariate stochastic differential equations (SDEs)

`[`

simulates `Paths`

,`Times`

,`Z`

] = simulate(`MDL`

)`NTRIALS`

sample paths of `NVARS`

correlated state variables, driven by `NBROWNS`

Brownian motion
sources of risk over `NPERIODS`

consecutive observation periods,
approximating continuous-time stochastic processes.

`simulate`

accepts any variable-length list of input arguments
that the simulation method or function referenced by the
`SDE.Simulation`

parameter requires or accepts. It passes this
input list directly to the appropriate SDE simulation method or user-defined
simulation function.

This function simulates any vector-valued SDE of the form:

$$d{X}_{t}=F(t,{X}_{t})dt+G(t,{X}_{t})d{W}_{t}$$ | (1) |

*X*is an*NVARS*-by-`1`

state vector of process variables (for example, short rates or equity prices) to simulate.*W*is an*NBROWNS*-by-`1`

Brownian motion vector.*F*is an*NVARS*-by-`1`

vector-valued drift-rate function.*G*is an*NVARS*-by-*NBROWNS*matrix-valued diffusion-rate function.

[1] Ait-Sahalia, Y. “Testing Continuous-Time Models of the Spot Interest
Rate.” *The Review of Financial Studies*, Spring 1996, Vol.
9, No. 2, pp. 385–426.

[2] Ait-Sahalia, Y. “Transition Densities for Interest Rate and Other
Nonlinear Diffusions.” *The Journal of Finance*, Vol. 54,
No. 4, August 1999.

[3] Glasserman, P. *Monte Carlo Methods in Financial
Engineering.* New York, Springer-Verlag, 2004.

[4] Hull, J. C. *Options, Futures, and Other Derivatives*, 5th
ed. Englewood Cliffs, NJ: Prentice Hall, 2002.

[5] Johnson, N. L., S. Kotz, and N. Balakrishnan. *Continuous Univariate
Distributions.* Vol. 2, 2nd ed. New York, John Wiley & Sons,
1995.

[6] Shreve, S. E. *Stochastic Calculus for Finance II: Continuous-Time
Models.* New York: Springer-Verlag, 2004.

`bm`

| `cev`

| `cir`

| `gbm`

| `heston`

| `hwv`

| `sde`

| `sdeddo`

| `sdeld`

| `sdemrd`

| `simByEuler`

| `simBySolution`

| `simBySolution`